{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "def load_housing_data(housing_path='./'):\n",
    "    csv_path = os.path.join(housing_path,'housing.csv')\n",
    "    return pd.read_csv(csv_path)\n",
    "# 导入数据的函数，方便多次使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()#数据集信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total_bedrooms 20433 有空值\n",
    "housing['ocean_proximity'].value_counts()  #查看多少种分类  ISLAND 5种分类，每个分类下多少个区域"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe() #显示数值属性摘要"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins = 50, figsize = (20, 15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 房龄和房价被设置了上限 housing_median_age  median_house_value\n",
    "   1. 设置了上限的区域，重新收集标签值 \n",
    "   2. 设置上限的区域数据移除。\n",
    "\n",
    "### 重尾 头高尾长\n",
    "   *  转换数据，把数据形状变成钟型分布（正态分布，平均值0，标准差为1）\n",
    "### 收入特征\n",
    "   * 数据提供的上游证实 是年薪 单位万美元， 提前对特征进行缩放是正常的，需要得知数据是如何缩放的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建训练集和测试集\n",
    "   * 训练集用于模型训练 占整个数据集的80%，测试占20%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 方法一\n",
    "   * 缺点 data数据集更新后，测试集和训练集将发生改变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def split_train_test(data, test_radio):\n",
    "    np.random.seed(42)\n",
    "    indices = np.random.permutation(len(data))  #对数组重新洗牌，随机打乱元素顺序\n",
    "    test_set_size = int(len(data) * test_radio)\n",
    "    test_indices = indices[:test_set_size]\n",
    "    train_indices = indices[test_set_size:]\n",
    "    return data.iloc[train_indices],data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test(housing,0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16512"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 方法二\n",
    "   * 通过计算hash值进行分组，需要保持每行数据标识的唯一性。\n",
    "   * 对样板设置唯一的标识符，对标识符取得的hash值，取hash的最后一个字节，值小于等于51，256*208，放入测试集\n",
    "   * hash相同，对象不一定相同，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hashlib\n",
    "def test_set_check(identifier, test_ratio, hash = hashlib.md5):\n",
    "    return hash(np.int64(identifier)).digest()[-1]< 256 * test_ratio      #.digeat()   hash对象的二进制表述方式 返回摘要，作为二进制字符串\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test_by_id(data, test_ratio, id_column):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_:test_set_check(id_, test_ratio))\n",
    "    return data.loc[-in_test_set],data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id = housing.reset_index()  #使用行索引作为标识符id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "4        NEAR BAY  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2,'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
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       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "6      6    -122.25     37.84                52.0       2535.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "6           489.0      1094.0       514.0         3.6591            299200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "6        NEAR BAY  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 基于行索引，不能删除和中间插入数据，只能末尾插入，否则行索引将要变化\n",
    "# 寻找稳定特征来创建唯一标识符\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id['id'] = housing['longitude'] * 1000 + housing['latitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
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       "      <th>households</th>\n",
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       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
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       "  </thead>\n",
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       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2,'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 从数据可视化中探索数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分层采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 抽样偏差：调查公司给1000个人来调研几个问题，不会在电话簿中随机查找1000人，他们试图1000个人代表全部人口，美国人，51.3女性，48.7男性\n",
    "# 你的调查应该维持这种比例，513女性，487男性，这就是分层采样，人口划分为均匀的子集，每个子集为一层，然后从每层中抽取正确的实例数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bfa19f1188>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing['median_income'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 希望测试集能代表整个数据集中各种不同类型的收入"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 创建一个收入类别属性\n",
    "   2. 不应该分层太多，但是每层要有足够的数据\n",
    "   3. 将收入中位数除以1.5，限制收入类别数量，使用ceil取整，得到离散类别，将大于5的类别合并为类别5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['income_cat'] = np.ceil(housing['median_income']/1.5)\n",
    "housing['income_cat'].head(20)\n",
    "housing['income_cat'].where(housing['income_cat'] < 5 , 5.0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     5.0\n",
       "2     5.0\n",
       "3     4.0\n",
       "4     3.0\n",
       "5     3.0\n",
       "6     3.0\n",
       "7     3.0\n",
       "8     2.0\n",
       "9     3.0\n",
       "10    3.0\n",
       "11    3.0\n",
       "12    3.0\n",
       "13    2.0\n",
       "14    2.0\n",
       "15    2.0\n",
       "16    2.0\n",
       "17    2.0\n",
       "18    2.0\n",
       "19    2.0\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['income_cat'] = pd.cut(housing['median_income'], bins = [0., 1.5, 3.0, 4.5, 6, np.inf], labels = [1, 2, 3 ,4, 5] )  # np.inf 无穷大 将连续值转换成类别标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1     5\n",
       "2     5\n",
       "3     4\n",
       "4     3\n",
       "5     3\n",
       "6     3\n",
       "7     3\n",
       "8     2\n",
       "9     3\n",
       "10    3\n",
       "11    3\n",
       "12    3\n",
       "13    2\n",
       "14    2\n",
       "15    2\n",
       "16    2\n",
       "17    2\n",
       "18    2\n",
       "19    2\n",
       "Name: income_cat, dtype: category\n",
       "Categories (5, int64): [1 < 2 < 3 < 4 < 5]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    7236\n",
       "2    6581\n",
       "4    3639\n",
       "5    2362\n",
       "1     822\n",
       "Name: income_cat, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bfa4358108>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 收入直方图\n",
    "housing['income_cat'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据收入类别分层采样。\n",
    "from sklearn.model_selection import StratifiedShuffleSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = StratifiedShuffleSplit(n_splits = 1, test_size = 0.2, random_state = 42 )   # n_splits  分成多少组   random_state  随机种子，类似random_seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "for train_index, test_index in split.split(housing, housing['income_cat']):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 11)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>286600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>340600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>254500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19480</th>\n",
       "      <td>-120.97</td>\n",
       "      <td>37.66</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>3.5395</td>\n",
       "      <td>127900.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8879</th>\n",
       "      <td>-118.50</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2233.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>8.3839</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13685</th>\n",
       "      <td>-117.24</td>\n",
       "      <td>34.15</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>140200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>-118.26</td>\n",
       "      <td>33.99</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1.8242</td>\n",
       "      <td>95000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.02</td>\n",
       "      <td>29.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16365</th>\n",
       "      <td>-121.31</td>\n",
       "      <td>38.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4157.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>2.7981</td>\n",
       "      <td>92100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19684</th>\n",
       "      <td>-121.62</td>\n",
       "      <td>39.14</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2183.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>1.6902</td>\n",
       "      <td>61500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19234</th>\n",
       "      <td>-122.69</td>\n",
       "      <td>38.51</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>6.6854</td>\n",
       "      <td>313000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13956</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>34.17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2.7120</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>-119.46</td>\n",
       "      <td>36.91</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>3.9141</td>\n",
       "      <td>123900.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11176</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>33.83</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>3.3831</td>\n",
       "      <td>197400.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15614</th>\n",
       "      <td>-122.41</td>\n",
       "      <td>37.81</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>3.6728</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2953</th>\n",
       "      <td>-119.02</td>\n",
       "      <td>35.35</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>776.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1.9830</td>\n",
       "      <td>63300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13209</th>\n",
       "      <td>-117.72</td>\n",
       "      <td>34.05</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>4.0614</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6569</th>\n",
       "      <td>-118.15</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>184200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "19480    -120.97     37.66                24.0       2930.0           588.0   \n",
       "8879     -118.50     34.04                52.0       2233.0           317.0   \n",
       "13685    -117.24     34.15                26.0       2041.0           293.0   \n",
       "4937     -118.26     33.99                47.0       1865.0           465.0   \n",
       "4861     -118.28     34.02                29.0        515.0           229.0   \n",
       "16365    -121.31     38.02                24.0       4157.0           951.0   \n",
       "19684    -121.62     39.14                41.0       2183.0           559.0   \n",
       "19234    -122.69     38.51                18.0       3364.0           501.0   \n",
       "13956    -117.06     34.17                21.0       2520.0           582.0   \n",
       "2390     -119.46     36.91                12.0       2980.0           495.0   \n",
       "11176    -117.96     33.83                30.0       2838.0           649.0   \n",
       "15614    -122.41     37.81                25.0       1178.0           545.0   \n",
       "2953     -119.02     35.35                42.0       1239.0           251.0   \n",
       "13209    -117.72     34.05                 8.0       1841.0           409.0   \n",
       "6569     -118.15     34.20                46.0       1505.0           261.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17606       710.0       339.0         2.7042            286600.0   \n",
       "18632       306.0       113.0         6.4214            340600.0   \n",
       "14650       936.0       462.0         2.8621            196900.0   \n",
       "3230       1460.0       353.0         1.8839             46300.0   \n",
       "3555       4459.0      1463.0         3.0347            254500.0   \n",
       "19480      1448.0       570.0         3.5395            127900.0   \n",
       "8879        769.0       277.0         8.3839            500001.0   \n",
       "13685       936.0       375.0         6.0000            140200.0   \n",
       "4937       1916.0       438.0         1.8242             95000.0   \n",
       "4861       2690.0       217.0         0.4999            500001.0   \n",
       "16365      2734.0       879.0         2.7981             92100.0   \n",
       "19684      1202.0       506.0         1.6902             61500.0   \n",
       "19234      1442.0       506.0         6.6854            313000.0   \n",
       "13956       416.0       151.0         2.7120             89000.0   \n",
       "2390       1184.0       429.0         3.9141            123900.0   \n",
       "11176      1758.0       593.0         3.3831            197400.0   \n",
       "15614       592.0       441.0         3.6728            500001.0   \n",
       "2953        776.0       272.0         1.9830             63300.0   \n",
       "13209      1243.0       394.0         4.0614            107000.0   \n",
       "6569        857.0       269.0         4.5000            184200.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "17606       <1H OCEAN          2  \n",
       "18632       <1H OCEAN          5  \n",
       "14650      NEAR OCEAN          2  \n",
       "3230           INLAND          2  \n",
       "3555        <1H OCEAN          3  \n",
       "19480          INLAND          3  \n",
       "8879        <1H OCEAN          5  \n",
       "13685          INLAND          4  \n",
       "4937        <1H OCEAN          2  \n",
       "4861        <1H OCEAN          1  \n",
       "16365          INLAND          2  \n",
       "19684          INLAND          2  \n",
       "19234       <1H OCEAN          5  \n",
       "13956          INLAND          2  \n",
       "2390           INLAND          3  \n",
       "11176       <1H OCEAN          3  \n",
       "15614        NEAR BAY          3  \n",
       "2953           INLAND          2  \n",
       "13209          INLAND          3  \n",
       "6569           INLAND          3  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114583\n",
       "1    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看所有住房数据根据收入类别比例分布，收入占类别百分比\n",
    "strat_test_set['income_cat'].value_counts()/len(strat_test_set)  # 分层抽样测试集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350581\n",
       "2    0.318847\n",
       "4    0.176308\n",
       "5    0.114438\n",
       "1    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts()/len(housing)  # 完整数据集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试集处理完毕，用分成抽样的训练集数据进行分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将地理数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 建立一个各个区域的分布图，以便数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bfa409acc8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 散点图\n",
    "housing.plot(kind = 'scatter', x = 'longitude', y = 'latitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bfa4063648>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将alpha设置为0.1. 可以看出高密度数据点的位置\n",
    "housing.plot(kind = 'scatter', x = 'longitude', y = 'latitude',alpha =0.1)   # alpha 颜色透明度0-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bfa3b2f0c8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind = 'scatter', x = 'longitude', y = 'latitude',alpha =0.4,\n",
    "            s = housing['population']/100, label = 'population', figsize = (10,7),\n",
    "            c = 'median_house_value', cmap = plt.get_cmap('jet'),colorbar = True, sharex = False) \n",
    "# alpha 颜色透明度0-1\n",
    "# s点的大小  \n",
    "# label 标签  \n",
    "# c 颜色深浅根据median_house_value值 \n",
    "# cmp色域 查手册\n",
    "# colorbar 颜色区域标签值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.image as mping   #加载图片\n",
    "california_img = mping.imread('./california.png')\n",
    "\n",
    "housing.plot(kind = 'scatter', x = 'longitude', y = 'latitude',alpha =0.4,\n",
    "            s = housing['population']/100, label = 'population', figsize = (10,7),\n",
    "            c = 'median_house_value', cmap = plt.get_cmap('jet'),colorbar = False, sharex = False) \n",
    "plt.imshow(california_img, extent = [-124.55, -113.80, 32.45, 42.05], alpha = 0.5, cmap = plt.get_cmap('jet'))  # extent X 和 Y轴对应的坐标值\n",
    "plt.ylabel('Latitude', fontsize = 14)\n",
    "plt.xlabel('Longitude', fontsize = 14)\n",
    "\n",
    "price = housing['median_house_value']\n",
    "tick_values = np.linspace(price.min(), price.max(), 11)  # 最大最小值之间11等分\n",
    "cbar = plt.colorbar()   # 取出颜色标板\n",
    "cbar.ax.set_yticklabels(['$%dk'%(round(v/1000))for v in tick_values], fontsize = 14)   # round(v/1000） 取整四舍五入\n",
    "cbar.set_label('Median House Value', fontsize = 16)\n",
    "\n",
    "plt.legend(fontsize = 16)\n",
    "#save_fig('california_housing_price_plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \\\n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY   \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY   \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY   \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY   \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY   \n",
       "\n",
       "  income_cat  \n",
       "0          5  \n",
       "1          5  \n",
       "2          5  \n",
       "3          4  \n",
       "4          3  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 房屋价格与地理位置靠海，和人口密度息息相关。\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 寻找特征相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.044568</td>\n",
       "      <td>0.069608</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.045967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.924664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.144160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.105623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.044568</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>0.134153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>0.069608</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.049686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.099773</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>-0.024650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>0.065843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.688075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>-0.045967</td>\n",
       "      <td>-0.144160</td>\n",
       "      <td>0.105623</td>\n",
       "      <td>0.134153</td>\n",
       "      <td>0.049686</td>\n",
       "      <td>-0.024650</td>\n",
       "      <td>0.065843</td>\n",
       "      <td>0.688075</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
       "longitude            1.000000 -0.924664           -0.108197     0.044568   \n",
       "latitude            -0.924664  1.000000            0.011173    -0.036100   \n",
       "housing_median_age  -0.108197  0.011173            1.000000    -0.361262   \n",
       "total_rooms          0.044568 -0.036100           -0.361262     1.000000   \n",
       "total_bedrooms       0.069608 -0.066983           -0.320451     0.930380   \n",
       "population           0.099773 -0.108785           -0.296244     0.857126   \n",
       "households           0.055310 -0.071035           -0.302916     0.918484   \n",
       "median_income       -0.015176 -0.079809           -0.119034     0.198050   \n",
       "median_house_value  -0.045967 -0.144160            0.105623     0.134153   \n",
       "\n",
       "                    total_bedrooms  population  households  median_income  \\\n",
       "longitude                 0.069608    0.099773    0.055310      -0.015176   \n",
       "latitude                 -0.066983   -0.108785   -0.071035      -0.079809   \n",
       "housing_median_age       -0.320451   -0.296244   -0.302916      -0.119034   \n",
       "total_rooms               0.930380    0.857126    0.918484       0.198050   \n",
       "total_bedrooms            1.000000    0.877747    0.979728      -0.007723   \n",
       "population                0.877747    1.000000    0.907222       0.004834   \n",
       "households                0.979728    0.907222    1.000000       0.013033   \n",
       "median_income            -0.007723    0.004834    0.013033       1.000000   \n",
       "median_house_value        0.049686   -0.024650    0.065843       0.688075   \n",
       "\n",
       "                    median_house_value  \n",
       "longitude                    -0.045967  \n",
       "latitude                     -0.144160  \n",
       "housing_median_age            0.105623  \n",
       "total_rooms                   0.134153  \n",
       "total_bedrooms                0.049686  \n",
       "population                   -0.024650  \n",
       "households                    0.065843  \n",
       "median_income                 0.688075  \n",
       "median_house_value            1.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# corr()  计算出每对特征之间的相关系数，称为皮尔逊相关系数\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688075\n",
       "total_rooms           0.134153\n",
       "housing_median_age    0.105623\n",
       "households            0.065843\n",
       "total_bedrooms        0.049686\n",
       "population           -0.024650\n",
       "longitude            -0.045967\n",
       "latitude             -0.144160\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * 相关系数范围从-1 到1 ，越接近1，表示越强正相关，\n",
    "   * 当系数接近-1，表示有很强的负相关，维度和房价中位数之间出现轻微负相关，越往北，房价倾向于下降。\n",
    "   * 系数越靠近0，说明二者没有线性相关\n",
    "   * 相关系数仅检测线性相关性，如果X上升，y上升，或者下降，可能彻底遗漏非线性相关性，例如 如果x接近于0，y上升。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA3BF80C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA69436C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA6971E08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA69A0788>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA4DBF588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA4DEC6C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA4E20708>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA67F7808>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA6803408>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA683B608>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA689FB88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA68D8C08>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA690FD48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA6B7AE48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA6BB3F48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001BFA6BF00C8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用pandas scatter_matrix 函数，可以绘制特征之间的相关性\n",
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "attributes = ['median_house_value', 'median_income', 'total_rooms', 'housing_median_age']\n",
    "scatter_matrix(housing[attributes], figsize = (12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 16, 0, 550000]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hPHxpO1/FfIwVfP/OOLwcSnNzS7OoDY2DRlp6SYRWEqkkw47ncFygbFAO8Ggln0jIExhF4pkxn/Pfp0E+BMUtg6Ui2gQESbAQrAsxnE4i0Sqs9tRzYj4REGvIU0keRVjnmBVBalU2xB3mF1xPmuBW8griJqyuEUHgRZKVe/F8fskjBfVRcG5FtaIGQYh1SQmRCMeURtDTAikljbVYGwT6s2I+z0Lc7suKZ8d8hPhoMZ/4iZjP7hMxn6/tDTialfg25uP9R4/5VO6RJdqJIU8UxjjmpWfZHvdjMR8PSobEk2EarP7KOPJIY7xjkMdkWj0W88lbz8LBtCLWkDjJly91MMbzizd3Hov5XO6nvLLVWcV8box6q5jPuGz45vXBYzGfXqYZ5tFjMZ+rw4zjZcVGJ6ay8Mvf3ONkVnEyb/iZK/1VzMc6y8/s9pBCkCeard7jMZ+9gXgs5tONNdbB7iC42qUUK/lja8veIAfhmRTNY7JoUZlVzEdrye4g5Zff+Dp//59/MObzWSYdfKoxHyFEB5De+1n79/8F/JfAvwqcXEg4GHnv/2MhxNeBf8ijhIP/B3itTTj4A+BvAb9HSDj4H7z3vyGE+JvANy8kHPwb3vu/1iYcfBf4dns43yMkHJw+63g/SsIBfD6z3WZlw93TBUVlUFrw6qhHJ4+oSsPbBxMSLcmTkFXVeM/1UYdYSmrnuDde0jjDsmiQWmCMwDvH3dOCfh7RSTTTZY0FNvKIu+MldWW5N10ySMMLv9mNsdbxl1/fZtTNHst281Lw/lHB4WSBqR1RFrE3yNBKsn9asDNM2esnnBUGrQU3RznvHC/oRYoo1qRCUjnHTjehcW6V7ealZ1k23BoXdJKIYRJxsKhJlOLGZk5ZGW6fLUm1Zmka+knMe/sTDmcVznrSWDEtDJ00ZqerubHTRUjJMFXcPi2JpeBgsmSjlyOkR1iBwbHVibl9NAtuDQvTZc2y9ry218VaUMrTSWNubiQczBsejhdMlsFilULy6m6PQRJRGsMoTUgTjbWOWRWEocFz63DGraM5p2VNHkVMqpo8jhj1YrQUzOcNcarZyiOkEFzb6LDdTb8Q2W6mcSxxZEKTRIqdQYoz7qnZbktnuLVf8HC2JI8jrPMM04hRP+bGIMdL8bnKdnPOMy0NtbE01q3es5/2bLfPXcKBEOJV4B+1HzXwD733vyaE2AT+D+A6cAf4q+dKQQjxnwN/nbDI/I+897/Zbn+TR6nWvwn8rTbVOgX+AfCzBIvnV73377Vj/jrwn7X7/zXv/f/yvOP9qMrn84bzRIVIhVjTebDx+ijHes/d0yWd5JGRu6gM10Y5kZI01j32vfOedw7mXBmmnC4biqrmx/tzNnoh4+yru73grhBw53TB7ZOCrW5MGmu6keLKKOf6Rr56cQBunyw4mIZMoUXd8MP7U3b6KVeGGf004sGkYK+fkrSZPbGSq/MxzrM/KaiN5+pGxuVhtsrguX2y4PbRnHeOF+z0UvaGKVu9hINJxXYv5sG4JJIi+OOtI0sUVWP5/fdPeTguOZyWDPOIa6OMb10bksYRl3tpcE15j3WO/WlJoiRSSrqx4mTRcHmQYlyIz+1PS07nFUII0ij48jfyiGEeY53jhw+mzMoGh+fKILg+BnnEpV6KpxXmkXrqPfPe88MHUxItOVvWaBniDzvdmG9eHdKJNfvTEkSIAV7Mijp/Lp4miD7uM/aicz1vzHl5AHxwkXYRxjje3p/wk8MFsRIIKenF4Tm72SbqfF7wvPfw83ScnwY+jvL5VN1urRL41lO2nxCsn6eN+TXg156y/TvAN56yvQT+6jPm+nvA33uxo/7i4TxRQasgeLSSVMY8evHbxIfzF+Lchwx84Pu6Cf6SLNb0rOf7d045nFZEkeLVrZRJYdjIIq6Ocnb6KYkaE2uF1oKdXsqyMtw6XSDbeTe7MY0NQVqtJL00ZqefMkgVwzziZF6jRKgj2uklK+G5O0h5MC64f1YQKcH1zRwtBfuTkr1+yp3TBUfzksZ7+mnEsm44mgm89+wNMrZ7CUeTil4Wrc57Mq8x3pFHiq/s9tjuJVhrkUoyrx3HiyXeeYadkL77u++dMSsN40XN9c2MS92MrX7CVnucSgiujnLePZrxJ/dnHExLBlnE3kYKwLQ09LNgOe6PC+6fLRl2Eq6OMlwbG7x9umSzG5NqxVYveeyeqNYNa4ylspqNTHE6NyRa8pODOZvdmP6F89uflCtBd55e+2Ta7cfBx5mrbCwPxsXqudprFw3nqNvjfd6cZWO5d7rkZNGw041x3hNrRW09273kcyfQn/ceyqeGoP9841OP+azx6eN5CuZiHKkyj3zBT4szVaaNVfUTjHWcFTWb3WDxbPViCuNAGqwPj00/ibi22UHgV9k1J4uaa8MMpSXeeY5nITbmXEjCANjsxHgPD8clSSS5sdVBAg8mBdeHOUIJYiW5Msww1tFLo9XxTsuK947n3J8UjJc1SkgGueadgzmLylI2ltd3+ySR+kDqnROwkcWM84ZpZUKsItHUxnM4qYi04GhesajMKkbTWMvRvGReGba/kuLx/PH9SfCnS8lOL6GfxvxLXxqt0uUbey6EJDv9lNNFzWY3IUsUb+wNKIwjjUIhbDfVTJcNTnmOZhUnomZ3kK7uSR4pHixqtPScLS3bvZhuGjMpavanJcNODDwu6HAhVT+swINV9WBccGWYPdfKeBqc8x+Y66KSe9aY94/mHC9KBEGJNsbxpe0uXgQX8ofNeb7fJArXyLuQlbnViREiuNo+b/iwhd4aj+PzdwfXeAwfxd3xNAWz00vCSsyEtM+rw+wDPuTzuWMlV+4eJQS1ddw7W1LWlkgpvrSdsqwsZ4sKbyNc7rk/LkI2jRa8vT9bCZksUhwt6tWKtpMorm7kGOc5nIZMgZ1eEK4PJwWdRGOc52heM15U3DtbcqmfkmjFZicmapWaJGT5HExLbmzmJFoxXtScFQ1KSvpZxFY3ZquXMC8NG1nMTj/hbFEjrcM5z04vQUmxmrebKI6mFY13CBncYZGUVMZxugwWmbUwSGNKYzmel4yXDdu9hKxdpT8YFwigkz1Kkp4UNc4GZRsrzWYnRovAKjGtDEezOlhyKiiN02XDtTTEP8rKcOtkzrVBThIJ3jsukRKOzkIWYqwl47Kgn+hQy9TYkHp7QdA9uQI3znP/LFghsVYvZAV9nNX8rGz4/p0xtbWhIDOLGC8MQjxKAW6MI+smz5zTeo+1Dq0UozzmdFlTlAbXgasXknQ+T/iwhd4aj2OtfD7HeBF3RxqplQJpjONwVlHUhpNFsF6yKMRTokh+6NypVNwcdcCDJ6zI57XBuiBAIy3pJJrjWcHvvHPMKIuJIsUro4x3jpdc38jIk4iyNpzMa17b7vHaTo+bmx0g+PjLxnI8rzmd15wsazayiNI6drKI00WNBO6fhcy+ulUeR7MKYxzH85rGOrZ6KUvjmCwaskiy2U24utHBOo8XcGOzQ9zGtaxr66ecY39WhcyzRPH6Tpf705Iv73SIpEJJwcmiwltHRRAoQoRYg0RQGddm5QXLBhGCx+er3UXZcDit2MgjKuMo6ppYCjyCGxs5WaKZFA0PxwU3NjvUpiX98fDe0ZwH44KH44JeppkuDVeGGde2ctJ5za2jOf0sJBhUCi4PMqwPMTznPaNOvAqK0x6TlIL9SXBd9tIoxKmeYmU8a4Hzoqt55zwPxwXzyjDMNUpKJkXNvLK8vtMJlmb77HVTTazVU+dsjOPhtESKUE83yDSDNOLmqIPWn19Kyovv4ScRa/tpxlr5fE7xcdwdUgpwcH9WoSQsG0uqJYvK0o31ajx8uNtDa8nVUc7DcUFlHFvdmMuDlJN5w9myIdaSdw4XSCHZ7KUY53j/tCCPFQ5Y1mZlZVjvV/UX566pw1nF5UHK0byiNo6TRc1GJyQu7B8v2BukCBlqJjxB6d0Y5Rwtaqqm4WRecW2UsZFH1I1FKMGNUQhA+7buKookNzY7IanibBmyvaTi2kaOEHBlmGGd5/6k4Hu3x2gl6CUxW92IjV7Cn9yfhNRjLdnJ4lCT02baPZyUeO8ZZjHXRnlQ9k3D/qTk8iClm0Z045CyvjfMOF7UdNJgHe30U24dL5gUNbEOLs39ScHZsqGoTFBa45JxUWOt42xZobUiSxRni4rGBWtmoxNzc6vLoja8/WDKO/tTZrXltUtdskjTtIqyNj6wbkjRKtBHVsaHLXAurubLusEL2Bs82/Kw3oOAUTeibjzOO2rj6SSaqJ03jhSb3ZiqCQH5Jy2E8+djb5ByVjTUjeNgWvHt6xufa8VzjvPrvMbzsVY+n1N83ODl+bhIBDqcPNYsa4OQAmcesSk8b+6L7rhRJ3DHJZFiUhq88NTGUjUW0xJ6eu9JtGJWNERpcOucr5bnleG9wxlKS2IVvlNSYK0jiRVX+hm0Qj1SkmXZ4Akuq6N53ZKeWra6CYM8JqkMP7gbLIQ7p8vAO6Yk06phI48ZddIPxLTq2q3iS0KEGiI8CCG4czpnWhiqxrLwntNZzU5/xDd2B3Rjza2TObFUbPcTLg8znPcUdYhd+TYwlEaKq8OMRWPwztNNI8rGcrSoWVSmdR0GSyS4H8tVQsZuP6Vp3ZyLqmFRG+ZVcCVWjedoXuEFbOThcyfx7G3kCKBsHPfPltw5XbKoDWdFjTXwzuGcn78xwnnY7adEbc0O8JiV8VEXOGmk2OklPBgXABzOKnaleKoVrloC251ewqwwWBeKNAd5vHpqw3Ojn+oKvvgMd9KILNE45ynaNOgvAj7JLMOfZqyVz+cUHzd4eZEl4bwaWgqBd/6x8U+bW/jgrz+eVSsGg6qxpJEi1UHQlJUNdELOgxBc2UhoLMyWFc7BG1eHnM0bDmcV06Lh/ZM5W52Ebqb50maH/UkIGl90qfQzzemioZtoThY1eazYn1ZcHobYj8MzXjZ0I8Wt4wW7vYQskrx1b8zRrOKruz1e2+4yLy3fuJwSP5FufDyriNokhovnMC8bHkxK0lhzeZhRW8/9swWz0iKBq6McYxybvYQ80VweZJwsanZ6aiVcisayaN2LTWN5OC0Dl15t8c6RRpI4kqH4rzbcPytItOTmVgctBXdPl0ExOM/JvORsFshT+0lEqiX70wrvPVvdhMvDlGVtkUKy3Y0x1jErw7XuJZpZYVBC8N5hydcv90kijdaSy8PsqXGIxroPXeCcp0TvT0uyWD01s+4ipBTttXTgg0W120/ZHWaMl80HCiA/yrPvCFX8X4TA/SeZZfjTjrXy+Zzi4wYvL47LI7WK+VjPY+OfnHuYR9w5W65Smy8PM/Ce43nNtY3gNnI+KJxvXR2QRophHvOTgzl107CsLV/Z6bIoLd57Lg8Tjucl3VijlEBLybsnC17b7rI/dewNUk6WNWVtKGrBz14bksShAHBaNfzw7gTRBs/3hjnLyrAwhskyWEaLxhJrRS+L2OhElBZS4TB44gvXw/pAD3R5mHE0qx47ByUFDyYFd5tiRakihaBoDPfGS+6Nl0wLGwowbZgn1SpYlq0QBjhu551UBucdf3h/QhqFONEwizme1ygR+NOKxpDqeHWvDicllwYJWazZ6WaczUM9z9Gs4uZmTi/VXN/s8MpWBytC3GuQKY5mFZVx9FKFdZazpQukss6jlORoWrE3CsWg5wklF+tq4MMXOOeCtDZ2RZh7niTxPCs8jdQqxudc4CyLlKQb68cKIj/pZ/+zxsdxlf95xlr5fI7xcYOXF8e9FngYPzD+3FVUO4dG8GBaogQkUejJczSruNRLsC3typVhRmXsKnbzsF3dDfLQe+fqRoYXktpYjuY1e4PANJzFirrxxB3JsjQ01qGUDOSnACJw5NlWoDsX+rNcHmVEMlSkO+fJYs1WFvFH9ybkkcILkAjO5hXzQcaiqhjmMc4+Kpp2LlScC0BL8dg59Nr4y1Y34ccHc05bCvytTowkWAXTwrLTDSnmvTTieFbzxtUBZ8tmJRQ3OzEPJwWzMrjXtnoZUgjuj0te3UrppDFF1fAnBzPSWDJeGPbHJaVx7PYT9uclUhEy3rZylBR0UsW7B3N2+hleeHb7GY3zbHcS+knETw4XaBliR5d6CVXrfooWcQ0AACAASURBVEPAvLJc3kgojWVRNtw9W4YeQnnEeNl8YEX+vCzJc0Ga6JAEsj8puH6uUD7ECpdS4C0ctIr5nPQytI94tP9nuai+iIH7dZ3Pi2GtfD7neFbw8sP8yh8W9LzoHjhPfe3nEb79XNaG22chw+3+uGTUCTQjm52Ye2fLQG+vFA8nS8rKcmWQh9V/aaiaMPZsXtI4T6QV4yIw+F4fdThuBRnArDSUteWP7014fafL8SKkYwshaAg8XrqNPe3PKvqp5s7JMrAKSChLy+m8Jk81X7vc52heEekQ7zqcVZiWgLJsBEmkkEJweRiKQBvrSLXi65f7HC9qjLMM0phLg4RYSd45nLOoLdPK0ksCBXWsJJcHYbx1geHgwbjgZBHSp40NhJCb3QiPYFkHhXs6r3ntUo/hVsLBpOSt+2Mq02Onm5BFGi0NR9OK7UGK955BJ2a7G3Otdc9VjePmZgfrPUmk6CQ68O0Jwc2tDkoKJkXNVichixWni4ZOEmE8DFLNW/eX3BjlZG1a9vmK/GlZks4Hpd04RxaHdOjLw4w7J0tmRbNK134afcv583jRCpBCcjgtEYKV8tqflOz0ktX+npXs8EUS2us6nxfDWvl8AfFn9Ss/6R6oG8ut4wUn8woHIVW2Ntzc6PDqTgdjHbeOFzgP37tzhnWem1sdLvdTlAxWTG1CvcmiaqiMY74wTGu3Sg/OIsUvfWWHLNFsAndPl0yKBq0k17c6HM8rfv0P7hBrSaQley3Nzk4/RRvL3aohafnFvAj0PqM8RgrBRq759iubZFpx96ygMiGNe5RHFMZRN47GOb51dUgvjait487pcmWlXd/q8Op2l9JY7p8VnC4aZkXDIIupGosFDqcVN7c67E/LVQFqbVyIJUWKo2nJuwczbm51uDRIiaQK10dLFsuaWMtwbkqy0Yk4mYVsOa0lpQ0N/PYnJakWFDW8fqlHUVnmZUM3idgbZmFl7UKVv7wg6JJIsTdI6SQKLSQPW0HfyyKcC4W/jXWI80zGJ1bkUgqc8TyYFCRKksWa2lj2T4LbNI5Cx9SrGxl7TylUfdrzGK6PJdFR2M+FTDYpBWXZcG9s6cT6p8ZF9UV1F35WWCufLxg+Cb/yB4oQvedwVhK1LahjFXqSIEM8ozIhsB0pxUYesz8pOZ6W7WyeYRYEzNmiomlXfXmiudQLzbDmVcNON+G94wWlcSgh2MwjpAjur6o2vH80p6gtu8PQ/O6tB1MyLTma15wtakpjubqRsdmJKWtHGmnyRLPZDVlVx7OSo1nNRh6RxQoBvHu84HI/JYsVtnIczavAh3buTooizpY1D8cFVwYhJpREkmEW8SeLOjTRyzS9RNM41yZISLSSq06zaSyJZWjuNykMZ8uaOFJcH2Vt2wSHjhSvbnVC1qEIimx3mDLqJCsCya1+zKV+gvew3ZEY7ylNwck8uANPHpSMiwYhBFvdmEZrIi2DVVcbTpY1vTRwynnvOZhVVLUhS6LVfr3zoHhqbOfe6ZL741D0u93SB212YirraFo32+VhFpgjPuR5vH2yQEnBwbTidFGz20+D+1MEWp3Ds5JlbdEIrm93PlIc6YuCL6K78LPCWvl8CniS9frD4JwPK2zvSbVajXnaPI11qxUlhAD5sq45W1TksV4Jhycf/vOspfM4iLEuZMJJwb2zBZlWvLLVZV7V/On+HGMtx7OKzV7MwaRECIlWIb02jyX3xgUni5KdXsZGN2ZRBcGxmUc8mFRtz6PQwEtJwbio2dICKUL8RWlJbRzvHy+wzlLUFq3aF1XAySzwpKWxwuE4mpcsC8POIME4TxqFwkMhJIvSUDaOk3mJx5NPwyp8UTYctS4gR7CUaufatO5wnTbymD+6N2FWNBxMC3aHeWgSpiRpJNntp7x7PKcsLeOF4WdvhI6eiQ7kpsezkjiS3D4tuDxI2eomXOonFLVn93LI6FJC0Ek0//THRyzKmqJx/MIrI/pZzNGsYlkZbAa7g4w7J8ECPZhVJFoSScEf353w/tGcJA5tBU5mMd+40mcnT/je7TnjZc3ZsqaXagZZRFE5am95MCkZZAatFG9cHTJZNiwWFVIIrm6ExYoxIc07UmJFY3Me78viZ6dDn+PJhYwUgsNpxfVRxvXNnP1Jwd2zQD4LcO80JLS8shmsyBeJI31R8EVzF35WWCufTxgXe6wrGfpvhJ47T0fZWH58MOXH+3Oc92x3E372xgbAB+ZJI8WDcbFaUY66MXdOFvzpgxlSBkH6+m6XXhI/FtgFuH284N54ycG0Ag+dWFM0oQp9Wlp2egnOe6alpW4ssRacLiqO5zWpVlzeCBxok6Lh/ZMFyyrU+pzMahaN5Y0rQ7RWPKhKvnf3jMZYxqWhEynyWHPPh7bA09Ky3UsYZhGX+kmIw3g4bWtiDiYl87JpXVAh1XirE7PVSTmalTDzbPUSvIPDSVCOr+/1UQj2NgIrgyJ0nBwXNVmkyJKIThJobK6awHMWOlKCt7DVDZX/98YV+9M6uBqN587pnJ/sz9joxGSx5GRe8zs/OuKvfPUSURSUsZQS6xzeeayzKCVCMkVL2d9Yh0cwKRr2hinGxRxPa45nFaNOEhIG8lC5XzaWt/dngGNWOoa55t60xBKKSjfjQEU0LSvujZcr+v5OohkXhj++N6FsDN+8OuT6ZpdESYzz/IWrQ7SWHExK9tsaIw9c6qccz6qVxdNPNbPKMC8NVR5xdSP/0MXTk3GOqmVsiNv42vXNDrOi4UbLbqHkYsXV92FxpDV+urFWPp8gjHG8dX9CqiVprClrw1v3J/ziK5tPfYmd89w/W/Le4YJ+ptFSsqgNf3jnDCEEeaxW8/zxvQl7wxDbub6Z8+BsyfdvnzEuarqpJosUhbF8//YZX9sb8OpOD+c8D8YF3ntOFxVFbWla4XBaGDbymF4atbQrUFrL6aJif1ZwY7NLnmgEno08Zref8WBc8s7hhKIKGWNbvYSDccWyafjB3Qk/e2PA+ycF2/2EyaKhbEKRaZ5q6tISSUUnUZzOaxrj2NvI+FIvxRhHN9f8kz85bLPTBG9c7VPbwJRwOoeNbkQvVTQWlpWhqG3bDwWuDBOE0Gx1Yw6nFcZ7PIKfuz4KLbLb5mhZJLl7umRRGN56OKasLcZ5fv7VEbiQDXe2rHjvaMkwi9jIY+6fLnnneM7lfkYaSTyed0/m3Bh12BtmvLIpefd4Tqwlh7OabhrTjTVZIvnu3RAfa5zDGMveMMTPjmXNDx9MqJ3n+kbOK9vdQIMzLenFmvvTJcfTipM5bHZSrDEIKUF4Thc1WkoSXdJLIsBzsmww1gXy11ozyKNgtfVSGh/asd89W3K6qBm09Dyn8ypYKJvZyuKZlobtbvxCNDZPxjkEoYmic75twR5iVOfp3XGbqi4Rz40jrfHTj7Xy+QRxscc6EALwddWmM3/wRbY+uNsQkOhH6ZnLOnRSHLXEi2msmZQFVWPpphFawZVRzlkR+sQY65lVhmVtmJeWV5qw6tZKsqgMxjoq4zieV8wqg7OeWIPqBiaCnX7Kg3HJINFIKfiZS32UlCzbeb6+l/HlnR7XNnIGmebt/SnH0wrrBUKG3vTGWs7mNZWxbHbz0Ic+khSVZZApTBoBjqIO7bxHeRSahVWGo3nFySykHL+63aUsLVvDlNsnc45nDfMi0OkIBGkkSNsOj9Oi4b3DGX90b8JGJ+HnXxnxzStDEGCtDzx0Uqy412rjuD8tOJyXXOpnTIuauycLvvf+KV+/NiSJJKNOQtUEl2EeaaaFYVkaZlFDbSWRlOx2U65t5Nw5W3LnbMGt4yVKhHTmjU4I8o+XJlAbNSFp4EcHMyIh2W8Vb7i2Ibtsb5iF5mfGcG8S5trtZ0yqmnnVMOpEOCfYn1Q479gdhvt062iBxbMoLUtjwAtG3Rhj4GBStn2XBIM0WiV4dBLNqGXCrq1FS8lWN+F4XjEvDcM8tMt4ERqbJ+Mc5+0SnhZ0fzIg/7Q40pNYMwb8dGKtfD5BXOyxfm6xKClWHQefhBIh/RcPlQmCwFhHL9EI8fg8sVIkbdMxrSTCQzfWLKtmVVAaqE2CiwcekV0KgjtQInDWM1nWIZakFBtZRKzkKpjfWM87x4FyRgpBHstQ7NlyoN0+WTJZ1hzOS2RLUbPZSSiNo7IOEGSR4GgeWgFrBQfzmqNpKHaNI0knjogixW4/5Z+9c8StkwXjZY3zgkUVlPF33j+jl0h2ujG9RNFJI/JI8XBScP90icczLy1ZGrW9b+AHt87Y7qSMOjG7GxlKCO6eLVds2sNcUxpHbS3OBRqYyxs5k6LhwdmSKxs5kVLMygYlYLtNVgg9ZCyZD1lfjQ+xM2td248IRt2YWAdW714acX9csD8JLA7WB/bpf/HeCWms0Fqx3Q3XbLxc0ljLjVGHh+OS/rkV2liyKOLrl/t0UxVYqZ1lq5uCCG7T2jpoLNOyZlEHHr9YC358OONLWx3SSNFLFG/dH6OUINES7wO9Tz/TxCpYIef0OX8W4s6LcY5UPjvo/qIB+TVjwE8v1srnE4TWctVjfVFXq1jNs15mKQVXNnIWtXks5vOt649iPufzfLNlFbi4anzj2pD3jxQPzkrGi4Z+J+KNS0OUDBZPrBV7w4zGOu6cFjTO82BsSCLNIJbksaJs+cY2OjHHi5rJsgk1P7EiannbIKRfT5YN1zdDS+njWcPBZMl2P6GXRnx9lFHWnm9eSblztqSfahpjmBaQaU0eGRpj+dP7M75+ddAyTQf+tp1BSi+L2z4wCzqJxDnHZq8DhFXveRsF60On1WlVczQpibVks5Ow04s5ntV8/+4pG92ErxjDIA1zXh9lq26hd04WnEzrQLoaBzfgKI/pxIpZadBCcmUjZbpomC0b4kjxxtUBszK4ER+Ml/zgzhlH09C9dLObkMURHs/xtKJpCkYdw+G0xHsYdmLunS5JI00/VUipWDaGbhrxg1tn5KlmURmGmca1NUJSCHqpJtGCrX7CzVGHL233uLEVrsfb+3OyWJGLkIBQOwdCYFsWBu89u4OMbqx4+2DO3dNFuE9JRFUHC/DGKGdvI3+M8ubc4nmapfGi1sfzgu4fNSC/Zgz46cZa+XzCGOYxv/jK5kfOdksjxTf2hry23ftAttvT5nly1fjaTp+TZaC+ySPNpbb25mLjsFhJXtnucN2mLdFkYH6+upFTNJbdfsrBLHCIIeBoWrGsDVdHGdc3u6Ho1FqkFKRa0U1ivn1zSBSN0FIgEFwZhC6f06Vh1AuFqKkWzOtTitqxqBs2uwlprEgjxXuHC+6eLHnncMYwC3GnTqrJU80g0VwbxSH7zcNBXZFriQGq2mJdcN8tKsu8sURa8eP9OXsbGVEkSZXgzknB1y5rjuc1X77UXV3vWGu+eXXIH949YzyruH9qeW23Sx4njDpxsFS1omgs758s2Mg1ZR2y8SbLkG7ufWCESBRESrM7TPnxw2ngp0s1DkFtHJPSMKssp4uarW5MEgVOt/cP57yzP8UjwuKgsXz3zphOEnGpHxOJ8/st2BtkaB0+39zqcvdkQW0ciZbs9JNQK6QUlzcS+kmbUXZW4HD86HBOLAXdNGK2bDiaVHxlt8uXt3q8fqmP1pJ+Gj32PD3N0gA+E+tjzRjw04218vkUoLV8aoznWZBSkCUfvBVPm+fiqtE5z/Gi5tXN7geo5y/60aUMQi6wEgtAsLeRrTi3rPc0jeVoUTEtG0pjmZUNyypmvAgxiusbOVPbsHQO07IObGQx292Ee2eBreDH+3N2ejG6de/91tuH/OjBFO8989pyOKnY7idc6kTcn9R8/UqfREvunC6JlWAjj9jsJkgVKvrzJMI72O4lzKqWx8zDlc2cxnlSLbl9smS8rEgizS9+acSsNGRRxLwsuDHKqK1lsqhYNI5F2XC6qPnW1T5aB/bn43mJMXA0LUMGYScijyM2OyndKCKOJEfTkkEecRjXQXHjQ5fW0tJPPLePLfPK8OpOh0gFeqKiafj2lT4/OVmwN0hII83NrQ7jZcPlYc6sMggst47mVE2wRm9uh2QMj+BSP+Vb1wbkF56L4B7NqYxlWgbG6KJqglt13lDVoafPKI9pbFCYWTcGDz86WLCoGiZlwy99RQcB3hZ8Xnye9ieBZkmr0In24bjAA4mWL936+DwxBqzjTp881srnC4wXoZ5PI8XNzQ7bvYTjWYV1nrKx4GF/WnJ/ElK4B5lGS0GeRMwr044W9POIe+OC/WnJwbRkuxsy5X74YMpGHtFNI+6dLfmdgwmNDSzUy8qy2U94OC5Z1qFPze4g5WhhmJSBvXp3kGKdZ7ysyZKIUdtt9NbRkmlZs5EnfOvaEOGg8Y7rmzlnywpnPWXjubqRh8p+JTie1VwaZhS14Xha8k9/fEQeS24fLdjux1gXqu7/3x8fkcSK6bIO7bOVweORQJ5qbm51yeNANbSXJVzqp0H4OTA2ZIVNFw1JorgyzHh1q8Pb+zOuDjPOCkOkBIMsIUkiBlnM5X6o9wlJJYKv7HbZn5b8aDLl/lnBsjZcHqS8ttujk0T0klBfMy0Mwyx+zCIxziGEYNh2Tl1Whm9dHTApDAezirunBV+/0ufKIOMHd8Z4Ybl9MmeURYzyiM1ezP/3o2OGWWjHfdGKsd5TNIZFZVdWTqIlSds8ED669fEiwvpZv/28MAas406fDtbK5wuMF6WelzJ0s+zEmsY67o+LdkUrudRLeHt/SqwAIbmyEeN8qNyv2vqea6OMOBIYG1beTVvLIqWgaSx3T5d4BHmkmIkmKJRYsZFpwGGMZ2EsjQ3U/0ezUN9xc7PDe3i+sddjWjl2ujHCB9qcWIc08so4YhRXNjL++TtHeGBW1ggpWFSKN672ABgvG945nOGdJ54JlFI0jQUEP/fKqO2r4zk5K1iUFiEljbFMq4Z+FjGbFiRa8aUdzbBt/7zdSbg0SDhdNhxOCg4nBZ1YcamfcGWYYRxsdxNqF5IZGhP44/YGKdZ7XtnsEGu1IjXtRJppUeMcjDrBHecQHM1K5qWl6oTFRC/1K5LPOycLnHPMahuuY23YzCM8UBjP3jDj6ihnWRte2eqSRIo3rg/53feOmZeGQSckH5zMGyZF0zbcg3tny1WSgfBwMq9JtSRvE13Gy7Y+6QWsj4uKEg97w4y8XRw9qWQ+TLB/1owB67jTp4e18vkc4OOa9E+uDCG4qD7KOGzIhkt0ECj9PObV7W5gCIiDFdFNNUVtGXUCZcvJsqFqa2MiLThd1ozSpKX3t8xqQy/WGN+65ToxZWMpDMRK00sg04pIKr6216FqGu6dhsC9B949LnDOcWUjY9SJA+9Z46gax94w43BWsShrskgiBVTGs9WLGXVihnnKeFGz3Y3xrsuiMrx7vOBSV1O0Dc1uHc+4MuyQJYp+qplmEZP9CaWx5ElML9EkWpEnikRLnPAMsoivXukzKw1f3u4Qa0EcSaz1jPIYpSRlbYLQjDXD1HBWNIw6MUJI3rgy5HRZYyqDUpLLw5SiNkRK8f+z924xlmVpftdvrb3Xvp1rnLhkRGZWZlZVV3dXT3d7Lq0ZYCzbeGRjMQYbBHgeECNjYYGQPC8IeyQEEmA0CISQ/IAw2OALCCwejB9sRgPYA8LjGc+0+zLdNV33yoyMe8S57uu68bBORGVlZ2VVdmdVZ8/kJ5Uy80TsHftE7bPW/r7v//3+L233mDeaZW04mDf89r05Nzf6bA1S6i5I53cHGfuzim8dLCg7w1Yv5fpGznKhSZSgn8bB+qLsuDYI5b3LmZpxkfAHXtrmYFZxuuiIhEAby9Yg5bxsaYyj6kL2e3NSXDnPVtpeOdFu9RN2hhnnq+67yNc4vut+vVysnXPM16Xgo0XDF/aGLBrzgU0mieTHWth/kMSA532nTy6ebz4/4Ph+U/rLJ8OyM5wtW06XLeeie+x5Gm05mNXcu6hZNpqtQYqKQpYzKzV3tvrUnSVVgY68NUh583TF2ydLGuNZNmFiPY0ijpcNQoKzjqZz5LHj89dGnC0D52266pACVBwGVr33GB9kyhelZtkYiiRYEcSRR3u4d16yN8q51k+xcPVkvgP8+tmKWoeFP9mVaBP8apIkYjsOggHnwTlQsUDFksRKhBScLlpypRhkMb1ByrU0omoKvnl/TmNa6lbTy2JWjeWVaz2GWYJHkMcRU9sRRYIijri9WdBPFceLlq+/d8GwSNnuZ1wf5ah1BuEFV5RoAE8YvkwiiZVrSveiQeuAOuopud5IY85XDcfzmkxJlq1Ba4uSkkRGrFrD/nmFE55URkx6CbNaf4BK8IGFW0le3RtxMD1hUXckSvKjL4xYNMEksJ/FpCpsAjfHOXkS08/iK0NC64KsuzeJv4t8/aj71XqPWW88kRAMcsW8avnt/Tkv7vTI4/ep2nuj7Jlf2J+lvtPvtni++fwA42mk9JcIl9M1C+yjnCYf/JlZIqm14KLsggVAF4ZecxUxUAorPONM0U9jzpYNh9OaOI6IpOBoXqNkKNNs9RN6RcpP3pnw+vGSe+cVgzzmc7sD8PD26YpBGjPuJRwvGha15njZ0UsjRB3UeEIIZqXBOsfRrAmLmPF86db4Sumn4mBn8GO3N/jm/pTZWdgQbm8VTHqKrotQUjLKYoy1WOtZVC3R5YS989ycZNStQ0nBqtaY9WIZR9FVH2VRa755b8GP3d5gb5zztf0ZxjpmlabWNriTxjEeGPUSfvSFMUUSc7JsuTUpQHDl/pnGkjxRGOuCa2kUZqZubxYcz2rmTUcE7I1yciU5WDTc3iywzlN1huVpSa4kwzyh1ob5muE2LhJqbUkiyVY/YZjG3BjlxCJ4EV0ujkfzhuvDnD/4uS2sdUgZftfTqiFaD5gmccSq1nTOXdkcaO++u8fi4P7aFfbD7tdIBIVipx2DPLzvKJJ01iLFB6na8GhH3WdpYX9W+k6/G+P55vMDjO83pa9aw8G8xljH6bJ7pNMk7oOQ0cufqUTA+9/a7HGxaum04+v7Mxa1ZqufsTNMsd5xfVxw96LiaN4yGWYIBFLCwUWFBKZVGHDcNZ5jERxRS2NoV4btfs6NScEwj/n2wZJJP6XWnkVlMMaR9mPS2FBbT24Mk17G/nqBPl92obF+UTJcs8AiEWTQl++l7WDatEDAtNzeCnw0hAABvy+NOF+29DPForV86caYXqL44o2Cb+7P2RplIGFZafbnJVUHWRwz6oXS4f5FibEOQfAVSmLBvHFI4WlawyCTDLOc/nrQtTWGed1xvAabni07bq5VhQHgWpOumXAXpeZLt8Z8WYw5XbYczgJItdSOWak5L1te2e2zLA2rRnO2bOjnocez2UsZrTOnS+u8jULx5smSaa3ZHgSPoM1+2LCWTfBlmreWngoltkkvYZirsPE0Oth/C4ilZGeQotZA1AcX2Ufdr7UO6sgsjq7e5/VxztGiCZlWHLFVJBzZtYss71O1VSSfuYX9USXwH3Tf6XdrPN98foDx/aT0VWv46t1pKGlFEu/ddxGCtXHcf6hEkqwNyIxzeO+pO82i1iwazfG8IYkFb50uOS+Dl8tWL6WfBu6ckoJ+Hq/p0cHh9BLNc1Z2bPUVvc0BRnvOyxYVxYiLihc2ewyzeo28gVmjiaXEWkc/UxhnaXRgoC1bs5ZgRxRxxO8cLfnM9oBUhQHTfhLx7YM5bx+XDPKIl3YCFmdaasrGUWQx28OUz+8OOFzUOBsm+PFByTavNV99b8bhtKTVBXfPl9yfN7TasWoMkdAYG3hk8zosytfHBYeLQCt462hBqe0VpuafeWkzeOV4WNQdv/XexZU4vrWOi7LjxiRnkERMq46XtnpIKYgjSaM9r+z0WDWOOGqZ1yY4v9qQmZVN+PO8NlStZVgoXrnWZ1KEeal+ErM7yrh7UfHuWQlSECGIpODWRszJouFgWlNre0XF8MCdjR6aQK/udHiPu8OUXAXiwWUG9/AiK3xYnDsTLMzLNQD2kp13WYIr0pgfv7XBwby++toXb4w+MNB6uck8jobwacfjSuDPSdVPP55vPj/AeFxK/zgRwiUw9LKmbqxDR6E5v1wbtG2un4wfnM84nNVcH+cMs5hvHy6oO8vJoqbShpN5S2ksRZKSxI6qc2SJpzGOXia4Nko5mDUsZjXOwivXRqw6yzBTIDzvHJecLR3CwYu7Aw7mDefLlpNlQxyBIyBwYhGe0rX1dNahreOl7R57oxyL42LVBuWeD3NOvrOUOsiIjXO8cbyk1Y5YBXM2g8d0lmVTB4dSKZB0XKw6rg0yZAbWOVrteetkyXcOlyzajjSO8fOK7xwH+4hUxaFX0WiyRHK8jLipCg6nNR5PaxzvHJfsz2o2+4pEKprG8He/ecBLOwOu9TNaa0mkZFgEllqtNdna+2fZaMZ5fJUdSAGNCZ42w1ySRpI7WwUCwcFsxeurDucdG72A07k2gFf3RgyzhNZYOmMpUoWMBIvGrB9aJNmavv3CJMwDNcZeJoKoWGIs3J1VV9ilcT+hMZZ5Y7ioNJEMasWHs+/LLLs1lqNFw0ahmFaavVEWHiAeKsEVacxLW32s91c9sH4SP9Ke4VlY2J+r2j79eL75/IDjUSn9R4kQtHXrPsX7WRPA3ii7wuTvzyouFt2VWZc2jnfPy7XxmGannxBHkiSGo1lLU1jOVi1vny4p1k/1e/2cPI6YFIoXNgp6SYS2wRTM2FBCqTrN/WkQFUQiuHt+53DJRh5ztKg5WzTcPS/DpH4k8M7zlRcnTEvNVj+o4W6sm+Qns4aNfsrBtGZUJNSdYW+UMy27QLAWgkXTsaw7EiFJo4jZKvjj3JoU7A1zLqqO+9OWYaZINnPunlWclx0HsyCP3hgkTJuOWhvOy5a6M2vfmoxpJWnaBuNCSUjFgjxOglJMehaNJlcRCHjrfEVVW5JEUqiY87LDOcfOMGyiy0ZTdZatXujnOOdpjKVaq96UFNSdzMEyvgAAIABJREFU4bf2Z9StZtlamNWoWHB7c4AkYnuQrIkKniQWXB/mnK46qi5I1DdyFewjrCNZl/OsDaWx+arldNWxbAy9NKKfhb7Xe2cVeH+V5UzLjotSk6v35dXnZccr/v179MEsO4kjdgcp2nuuDVL6WeiTPapkLKUIareH7mWlnpwd90nHc1Xbpx/PN59nIB41Zf5hT2CXSrXTZYf3ni5yARjq4foo5/68Zlqu3S+rDs7gxkbON/bneDxpLKlaw3eqjkmRMK8t4yLhZNmQRAGMOsoTBqni1lbBIFPcnVacLTuKJOaVaz1SJXnndIW2ntNlx6QXI0USBkmt42xRs38RFHAIQRKHTGZ/TVZ+76zkznaPnX5Klip2+ilHy4br45xKWwZpvLaKhnGhuD+twzkIaiulIoy3HMwqhBSMckWaxLxxumRRGzpjKLOEad0xLlK2+oqTZY3DMy4SIhmt5c7hCV3K0INpDbwwyZFr+4JOe2LlWDSacZFwY5Jx76Li4KIODwDWs6Uy3rkouTnK1+8V7l5UaO2Ra2n14Sx46Ex6ocRYpGHjvDkpyJOIo0WL9Z6NfsIkTzgvO3ppjEeQqsDgs87zxumSeWPYKFK8dcxqzSCNGeYKIcKM1+G8odWGb2vL7jBlo6e4f9FwPG/IU4kxgotas2iDr5J1nnGhaI2j6kIGtdlP8Jcagwey7F4a01nLtNaMM4X4iJLxD1M28VzV9unH883nGYvHPYHhgnopjeWVS2SjHddHKTc2wpzGyaJlkMXEkeT6GN49r6jXHj63Nnt479mfVWwWCXkSoeeOw2nFuFBEMqdII/ZGYc5me5ByXmpe3OyRxTHeWd45KxnnYXP4A5/b4o3DJWd1x/1pzYvbQ6aLhoNZzbLt2Bnm9FNF1Vky2WGEwDnHYaVpncN4zx/74h6nq5ZYCpI0ZqOXsDVM2emnxLHkdw6WLBpNFgcvnaN5S9cZxr2EXhoxyhWFkjTGc1ZajHUsW8tGIdi/aOiM5dB6hA/W4EUSUShB3TiEhB/ZG1F2wXbi/rxls69YtNB1IXtJ4gS8572zFbPKkEioGkNjHbEQbPdTZlpzXGruTDIulh3aePYmKXvDAiklJ7OaL90YMu6lDHLFstaMeopZrbkoNbMqqO4OZy1niw6B58dubdBYz/605tZGwfYw4et35+QqZKKH85o3yo7P7/QZFTFlHWZzrHVcG+XUxpGqKGS5w5BhOi9IE676foezmu1B6OmNc4GQITO1nqtF1655fw7P/qxGCqg7S+9axJ1Jn5Nl+6FigR+mbOK5qu3Tj+ebzzMWj3sCe/DDHEdBtTQvO25uFORpHLyBHogkjtjqJ2z1E1obynoIQa5iEIJWW4xzdNZD50hVwk/cHjDMwtcjKelnEdMqDAe+eVoGFtz6afneRcP1zYJdm1E2AadzuOzY7KnAXlMxnQ5qsbNKo5QklZLbWwWRlCxrw2+8c0EWSYZFEpYjD86Ga++MZV5r9kYpi9ZSd4Z5o5kMM04WgUN3smh4ZWeIE46tnqK1Mf00ZmuQcrpsuFhpVCRpTPBamtc6gDmTYH2w2U/ZjVK+c7RisxfhfMRmT7JsA8A0jSSTfrDHfu1wSdV1ZGnMUIk1YLSm1o7+RLI7zpHCU01bboxy7mz1cdYFwngkMc7hPJyuOrS1vHtacm2UISUsKkMRC4o8mOvdX7Rs94Pj684w5WTRMq06IuE5X7bkacy06jAmiCyujTI6J0mTmGujnMNZzdmqA2BnkNFZj5Rh/knbgOjRNghG1HrY05nvlldHl/ee9Qg81nqkCFYamYq4Oc4/FKL7w5ZNPFe1fbrxqWw+QogI+E3gvvf+jwshJsD/CtwB3gX+Ne/9dP29vwj8GcACf857/8vr138C+B+BHPi7wC94770QIgX+OvATwDnwp7z3766P+XngP1hfxn/qvf9rn/ibfUx8HJLBY5/A3PtzEcZ5DmYVjXakccSNSUESBZnsrO7obFCz7Y3yMEzoPdNa03SWJJa8ujtASsHdacWL2wVb/ZTzsqNsLS/v9NgeZCgp+Cf36tAPUIEKcFFqXtouyJWi7iw4yFTM7ijn7ZNVkPcax2bhA6BSwbyyZEqgRDDGa7Rj3FOkieRi1ZImknGegGQ9DyKZ1x3OebJEXk3GG2PBe4ZZjIoF/TKibA2LpgPvcYSh0sZ57k9LOms4Lx3DNGFnlARFWtXxo7fHYbp+0dJoQ2cjchWxaCwrb2hsxI/dHnO+WpfDekmwmrCOfprw8lbK8bKlasNTfKYk56uWb9ybEQvBuJfwO/dXVJ1jb5RxumyZl5pYSXoqopfGXB9lXKw67k1rlk3HW8clAo+UC169PmYySJF4Vo3hYFqTKIHwnpNSUyhJkSqa1tIoyUSlSA9lZ4PfkAlUiv1pRWc91nlujHOOFw3R+qGl0xZtPfmaov5hi66Ugq1BgMdu9MIDws4ww3soO8P5qvvQ3uSD93LTabwIpeFneVF/FsQPn2Q8S4DUTyvz+QXgNWC4/vdfAP4v7/0vCSH+wvrff14I8QXg54AfAa4D/6cQ4rPeewv8N8CfBf4RYfP5Y8DfI2xUU+/9Z4QQPwf858CfWm9w/xHwFYLC9LeEEH/ncpP7tONJSAYf9gR2+WE+mNW8c7pi2Ri2Bgmnq2Ad/cpO8HxRM3n1tHl9nAOhXDfKFaNM8erekNNly92LKrC8xjkqDuW2k0VDpS1fuzvlvOxYNBrnPYM0TKovGss392eBvTbIyJOAoQkT8bBoDS9u5rQm43BW0XaWG6OMXhazbAytNhgr2OgpzuYd1vsgZNCefqaoWsPtzYKTRYtznumqQ0hPq6HTYROqW4fxoHVwcCVUJKkajZCCYR5zsGgpO82kyHlxp2BaGYTzVFrzub0RF6UmBl47W5HHQUjhnEULSSzgbNkGu+llx6oNrqN3Ngusd+SJ4mYcsqPpssZ4SJPgeho2X89mIXj7ZEnkQx+pbB1l2/HGtOKP/MguRRKzO845XjVo4+hlMakUVNrwxuGCVavpvbhJpiSvH684L1ti4ZmtWto0pkgVd7Z7rFqLNo44lljv6CWKg3lDZxzOev7plyc4H/BFG71AuF5UmvMymPvtz+qre/HDFt1eEnNjIycSkKgI54L672zZkjyCdg3vz5ZdGtUFojqcLFt2pXgO5vwBxLMGSH0i2YkQ4vcLIf70+u/bQogXP8YxN4GfBf77B17+E8BlFvLXgD/5wOv/i/e+9d6/A7wJ/KQQYg8Yeu9/zQd3s7/+0DGX5/rfgJ8RQgjgnwN+xXt/sd5wfoWwYX3q8WDjtZfGqEis+Vf+Q4+5tDt4+OkkUxF7w5AJ3d4sGBcpmYo4WQSczSW9+sXtPnc2g5vl5WZ2e7PHS9t9xkVCEkvubPb48s0xgoC0mVYdF2XHW6cr7k0DdHRWtqzqMNlftpav3Zvy62+d8xvvnvON96Z87e6URWXY6CUM8rCAdTY8ae9tFOyNUmptOV10CO/Y7AfEzLzqGPdS8iTmvNQcLWqGWUQUwf6sCq6cSlBrzWsHS04WNavW0FOSi7JlVXWcV0GtZlzoY6gkDkqv2tAZh0DSWcObJyV4x+44RUWSf/j6Kc47VtqxKDtOl+H/xbiXMm86VnXHd+7PcTaUOPNYcrZsMdbikaSRp2o1Z4uaST9nUqTEQnC+bKg7yyCNyLMY6+FwUaGkZLuXcHuzz0aRgAsDN8Y6Yg8IgcBzsmo4nDbMO02jLXfPg9XERdUQS4jjmNtbPSa9lM/v9omEIJZQG8e7pyWtdnjvGKaB77YzTNmfNuz0U16YFLyyM+Dl7T4qltyeFEx66ce+F6+PczyCugsZ0/YgxcOV0jLI4wPw9O5Fxb2LirsXFVVrOFm25Osy58f5ec/j6cf3sgZ90vGxMx8hxGUW8TngfwAU8DeBn/6IQ/9r4N8HBg+8ds17fwjgvT8UQuysX79ByGwuY3/9ml7//eHXL4+5tz6XEULMgc0HX3/EMQ++rz9LyKi4devWR7yV7y2eduNVSkG8xqR82NcfPu+Dr2nr8EAvU+x4z/mqZdkaVp2hn8RMa82s7ljUHa1x9FKFtpaTZUskQKmIrnO8cbKks5ZbGz2cEFwbFsxqSyTheNEwW7Xcv6g5XDYB3+IdN4cZ/+wX9kgiiCNBksdICVVj+cb+HIDpquXds4pRFvOtgzmTfsKLW33uzypK7dkZKM6WQd6dqRjjPVEkSZ1jWVu0Cx82QUC9rOqOcREzrTRFFvOdowUyEqwaw8Ygw1jPtDbEncM7z7I2RAKcgDvbBe+erdjoKc5XLY3R7F9Y4kgEZyQJVRtKmUkkwXu8h8N5izWWbx3WlG0At+4MUjZ7CQ44X4Xh31euD5i9M2OYxpwtWoZFjLaeqrX8xttn/MSdMU3rkBI6axikEVvDjI21YOT1oxVSeGIZ8dJOwXmpiWPBjXFGqmJWjWZ/WvGZnQEyCn1DKQSJerJ78eH+jpSCc9F9oJ8j4AHMU8iGDmY1AsiTD5dkP+14lkpLz0o8i+KPJym7/UvAjwFfBfDeHwghBo87QAjxx4ET7/1vCSH+0Mf4GY/6LfjHvP69HvP+C97/ZeAvA3zlK1/5RB4DnnbjVT2it7MzSK9oxh/3ejptWdSG66OMzUGCMY63TlZo56lbQ9NZpo3my8Ocjb7iYBpApNp5zNqy+XzV8N7FikGRoI2nSCSzskNJgYoEJ1XLrGyIo4hESKa14fXDOZNexmTg2e7nOA91Y6i0IfZwuGjYQ6CkJIsli9qy7DpqE4Za90YFO8NAuR6kMRdlF6wFtGVadlSdZ5jHDPMY6yy1tixKTRJFbA4SXtjIKRvNrY0eZWc5W9V4q9EuYpDFGAMqiVjWHe9oy7QMCJ9l50ikYFQkpJGks47pqkWpkMldW89CAYyyiM5JXs4S5lXH/VnNtzz81IsTjhY1y9pyvKiJJCSx5GSuOa8040Kx2UvY7ieBczfvuKhb2s6xO8rxgPSwOwqii5vjjH6e0GrLvYuG82VNYzwvTHp4Gsy6RBbHkt1BGFh13j/xvfioks3Dvckg8mg/kA0hLB4+NdHBs1ZaelbiWRR/PMnm060b/B5ACNH7GMf8NPAvCiH+eSADhkKIvwkcCyH21lnPHnCy/v594IUHjr8JHKxfv/mI1x88Zl8IEQMj4GL9+h966Jh/8DHf61ONpy3jlFI8srfzqPM96inw8nr2pxVla+hnMTeGgatWaUvdWqouLBrjLKbVhn5WAB4lIxptUTJC4ugnivNly/1pQ5FGXN/I0cYHWnUeo7VFyBjvHCpeLwJCsLuRMa+Cj46x4WvWObIkIo8Eh4uKprNEccROnjLOEo5nLfMucM9Olw1nK82NjZzj9XCmtrA5zEgbRyo9VWvIE8mrewNaA8bB3fOSL93a4O9/+4jDRQClKinJ0wQVBSVgHVlGWZjgPy9LhlnM3jBdAzk118cJnQnZSNkCXpAryeev9fns3pBVY0hiydunJS9sFbx2YHh1b0itDffnFf/orXO2h2koDQrBKI946dqAQRpjgwUO985qskxSrlFEnXTU2tJ2BgT82lsXLGpNmkiWnaUzAS7bGUetPYezijgWDNKQIbWd4Ze/fcSkF6y2N3vJFZPuo+7Fx83rPNibBL4rG7pkxT0oyX6cJcP3Ez9Mc0WfdjyLUvIn2Xz+lhDivwXGQoh/C/g3gf/ucQd4738R+EWAdebz73nv/3UhxH8B/DzwS+s///f1IX8H+J+FEP8VQXDwCvAb3nsrhFgKIf4p4NeBfwP4Sw8c8/PArwH/CvB/rzfJXwb+MyHExvr7/ujltfwg4mnLOC97O48734eZel0dP+mBZw27DLbJKpLk/YhroxwVBaHBojN463lhs0fZGt48XWG9QcURTkAUS754rYcFZsuWfhYTx4K6NVgP1hoSFdPLJJGI6YwjiSS3NtMgy7aOd89XvHUSBmSPFiGjeXGrRz9V3J81fEb2SFWohU1LzWuHKwRgjVur5GCUJSSRJBZh8Z+VHVp74igmko5ICs5Ly9ffm1KogCBqtcHIACaNo4iTeQ3W8fZpifcW54My8HBWs2o1ZytNLKGXxmRKBMhnERNJycmyRYoFO6MCKUFKz0XZYDzISFKVFus82nuyJEJb1vQBRW/NavvG/oyzZUtjLC8kBTe2exzOGq5vZlTWIhAILzha1OA9vTSjNYbX7q/YHSXc3CxIpOSNkxWJA+s0W63hYFajpGSjSDDWsagNr+wEZt5H3YuPK9moSH6gbHMpiClbc/VQlKmIW2tkz0dZMnw/8SyWlp6leNak5B978/He/5dCiD8CLAh9n//Qe/8r3+PP/SXCZvZngLvAv7r+Gd8SQvwt4NuAAf7dtdIN4N/hfan131v/B/BXgL8hhHiTkPH83PpcF0KI/wT4x+vv+4+99xff4/U+lXjaMs7Hne/DTL1+/NbG1QYUx5Kbk4KjeUPdaYSU/MHP7fDb+zMuSs3RvCNPNdcGGXe2+xQqYlpqvAvGco02nK0C+uZw1nBzsyACvrA94LXjOaeLlkmRIAkS6rJ1bBawO84RMiKWMK07BknMwaxi3hg8gYhwVnaczhu2b2a8en1AJCXjXNHEEceLjlGmsARkDVFEEQtaY9kZZuxtZJzMW06sp5dF1F3oQx0uSnCCLIlIk4TTRcO8MWRpTJEF+/BaOxpjyBPBC5MR1no67dZYHU+RKKJIcG2YItYEgncvKozzrCrNsgmonM1eirGObxyuaK2l6yzXJzmniwaBJ4klXhi0tSRq3Qvy8PJ2j36iECIMy64aw/WNlCJRHB3OEB4m/RRB2NCatUurcY5BnhCv+zGFkuu+UPDrOT1uuLPdDzDPKKbsWrz4eJnHk5ZsxEN/fuCefIg3+DjrjyddJJ/F0tKzFs+SlPyJpNbrzeZ72nC89/+AddnLe38O/MyHfN9fBP7iI17/TeCLj3i9Yb15PeJrfxX4q9/L9T6r8XE/lI8y9VrUHQfzmpe2+h/Axd8c5zTGrtVTgs1ByrTUbPYUMpJEEt44XuG85a2jJZmKwqxOEcpPnfE0naY1wQtoVKRsFAmLSvPKTp8ImDeGXiF5eWvI1iDnaFHx2kXLpJdgC9g/rwOdoVBcG2aMC0u/SBjkiiyOWTYdB7OWsu3QJgyAnlUd4yxBa8tGnrLVT8MAZSewzvLKTp9Brrh7VnK2atkeJlwbpZwsO0Qk0T70iaJIoLWjqg3g2O4ltMaz2c/op/Eajir43KjP9qigs+F39frRgtf3lxjnGaQRRSqvpM/3zkuEgC+9MGKQxnz93pxv7c8QwCCNWVQ64JG0Q2tHrR0qBhVFbA9TelkY5m1az63NgtNFQ9U6zsuO42VHf1bzuZ0B41xxbZASrXtsZWuQhAb/3ihHyCCMcEARh8W5WbuUJvLj9Qg/bsnm8oEnlgIVx/j1vy/Lbp2xHC/aR1p/PLggfq99m2extPQ8PjyeRO225P2GfUJQu5Xe++GHH/U8njQet7k8yYfyQVOvXhaAkZdY/Qc/7A+es9SG43nDybLFC8+LO30a7TicN/SV5bwMc0Xbo4xBEbFsDEJIlk2gISdxxLVBivNubWNdMcxjjAsNfCWDVFggWNWGVa1JI8nxtKLRNgA3o4hFo7HreZSLZcsgd5wuWjIVcTTT7E8bkrUpW2cdxhlGRcGs7JgMEyLCItw5QdVZnLMYaxlmMavGMa8c/RTuTku8dyHjSTTaCl7cHpCpmLOywRHQMpv9JJT0lOB81XE4qzicN0yymEQJ5vOOk0WAoUZCcJ4qPMGietVZbm/12B6mvLjVI1GS02WLcbA9SNgd5WRJ6HedzSxCeMrG8N5ZR2csd7YLvnRjxN9fhvKpcZa6spyuGnIpeXmnT5ooPrunePukRBvH7ihjs5ew1c/WC7wlTyKWjeV81RJJwRdvjJDyfeO5j1qgP07JxnpPrQ1la6/u0TyRHMxD5pjGiouy+y7rj6fJg3vWSkvP48PjScpuH1C2CSH+JPCTT/2Kfkjiaco5L8/1uHq4MY79i4pUySsr4oNZzY1x/sh5ICkFu8OMt89WHMwDQn+cKZokwltP6yzOeQ7XZRApJEfzGolnkMesqoZ3zmoiHN+8NwtQU+O4OcqxDsZFxtlqgbWeNBYM84RxkYAUbA4SDi8s4yyi1gaBQCMZj2Jq7QKY03mGeUKhBL/17goVRXTWcDqvWWrDl6+PGfdCD+fd04rdUUptHL08Ydc5rIdWG7w1KBXz7ftL0kgyazqySGIcNFXL/rS5yurKzjHMYrS37M9rnPd4L6k6i547emmM85Ankt0oB+/ZG2Zc38iJhOBr96a8cbzEWBcGN4XgYqXDfFMkSKNwLukdx2XLuEgZF4rTRUulNa/sjBjmCZN+Ql/FpImk6jyny4Zp2TGrGo6XHd4FBt21YcLOMFC9z1ca52FrUKCN5aJsEFEYGVLSc++iIUIwyBS3Ngq8EGjn0c4igJd3BqQyWFAkUmK85+5F9UTZxUeVbIQPzrZZ/D4h+3TZcmOYE2dr8vo45+55xbIOZO5Pggf3LJWWnseHx/dMOPDe/+01neD3XDxNOefluax1wbhslNF7yB+ls479acX9WU0/i9nqpwDcnwYX08sP8YPX0GjLyaoNw4zO008E52XHqjMczhs87/usfPbaEGMtb54uee1gQdUZutYx6MXMy45V67gzyRBErBpDlkTsjFKkHzBvO+6eBaGANo7dcU6qIua1oTWOaaXZ7KXEAiyS1/bnTJuOYRazNcg4mVccLWsGSrFRKGarhp1ewk+9PGGUpRzMazrnOC81UsAoi7l7aim1RhvBOJdI7ZjXwelTAyqXKAFvnJZ44YhExGYvpWoNm0XCzVHGG8eaIpFYEzhllTb005hJEa/FEDlpfNkwj9koFIeLhkQKVp3ljZMVK23ZLFK0tWHmaN1La63jxkaweCi1YdG29FTMuFBUrebN05IXxqEkVnWGeWMpG4M2lqq17AwVe+M+sRR47zlYtMHGu9b0U0drHJmKcdZjnOVXXz8N8uxhwSiPWbSWjVxxY5zTrS3WT9fEhsvF/uiifuqqMC+Ciq7SAXIayQBeFQ9Yf8Rrccf1D3loet63+b0TT1J2+5cf+KfkfWzN76l4mnLOh88lBUxrTZ7GV098en3+NJIBs+88J8smyJOj8KTrvP/ANVyeVxtLbSyRgK/tz3lxu8fZrKWXRBgLk57iaNGiEAgJ3zlcogBrPUZ4Duc1m5kiSyN6eYrUQe01KmKEF/zUK5t8+/6czX7GqjUcTmsO5zX/5G1Ha+CV3SHFLHj6FGnMQEmO2o5ZpempmFXdce+8YiNXKBmxai0r7djdSEM/yXriSPCZnR4Owb2TBW+clCBCT0NIw0objNY4BzaR+Mhxf1oRCc+q1WSJYquviFXE2arl5Z0+u3nOotEYa3EejpY12oCQnvPaIiqNlIKf/swWeRKyzLvnJSfzmsNFx3zVsGw66jbQFTaLiDxVjHPF6tIXp7VsFIrPDvq8d76iah13ZyUHF8E19M52n6oz/B/fuEBFIZMc9BX3pxXTlQgzOp3mtNL8zOe32d3IiO8F8nUWCRIVbCpOl5pF3SGl4MUtSS8NvT0vVCgBrrp1g19e3at7o+wTUYVFQpAnMf0sXuOWPNbxXVLrvfUDyqPied/m9048SebzLzzwd0MAgv6Jp3o1PwTxNOWcD57LuWDd3GmHcx5HyKogDAXmScz2IOVkTQ6IIsnLO4OrEsOD12C9x1rHvDEoKVia4PWyrMPT9WHVsTsq6KWKrb7jnYsyzId0Br8WKXgPtTY4GxaEzcISRREbBdwY5+wOEt48XPHeWcVoDd4c9xIWteZo2aKNJ0sDVXtWdhRpxLQxTEuDFJL9ac1mHjGrO7b6OUoJJJI0FmwUKtgy1BbjPNoorPcsO4cXHiE81nisDU/EdWfo5/HaUlxwb9kySIOqLY5EIHo7g5SS7X7KoEgZpjH/7+unGBvoAFLB8aIjj2tGvYRF03FvWlF2FmfhZFWzagzWWI6XLfNak8SCURZzuOiw1nMSS/YmBbGAcR6zP6uptWFaW64NU5ZNh8Rxtmg4nNYgQuay6BwaQd8otocp5+vmvHaCzmr+4Zvn/L7bI17Y6tNblTgkiYA0jsliQT9PwcP9eYNb43quj3L8eqA0joJxHBBk97wPqA2Gb0FQ+v1mFw9uHNq7D1QFLqXWT6u/9Dx++ONJej5/+pO8kB+WeJplgYfPtVEoDucNtbbEUgbXx7X3irFh0dDWBd8VoDOWTEVX1yD8+uvrcpqxgaV2tAigybdOl2Qy4nTVMMgTvA8ImlVrGeUReRJxuuiYVW0gVgOyJ9haG5wlEqI4QkrJbx8uuSg7ZnXguh1MK85WLVkSs9lXKCkx2jHqKXYHYTFfAqu6DdbPtSGNElQcsWo0beXZ7ik2BykIwdGsRkURgyJhUES8fVwGUUTryJREeIsUjqp1xAoypeilMUYHh9fNfs4GgsN5zaxscCbh1RsDVBzx2d0+b59I0liy7DryCA4WGoHjtc7SSwKyZ77s+PyNMWVnePOoZJhFJCoKpbgErIYiVZyuWiptKY1l3AZL8t1RzguFCvbRqeBgXrOsNK8dLtkeJvzmexcYY7goNcMsZL2zVQMyyMNvTHpEUvDG4ZLpquNk2VFEgkalFEpyuOzQVcO75zHjIuZw0VIkERs9xY/fmlCk8VVDv2w007Xc3nrP9VHO7ijjvbOSk2ULwM4wpbOOTH5/MzePg+I+ycPZ877N7/74yM1HCPGXeEx5zXv/557qFT3j8TTLAg+fK5KSH7+1gYrlBz64u6OMw1nN/jQMFhZpjNaOb+zPeXV3QJEqxoVif1Zf9aEmRcLRvAHn6ecxJ7PQkM7TCL+C/YuSQRZhnGezr3hxu09jHP/4nQucs6g4YqNIqTvDtVGBsZ5hprhYVbwhY9FQAAAgAElEQVRzWpGosNCkCr51MOdgHqjFe4XCe0HZWt48XjIZJGQq4vWjMmBwpGOQJKRJTE9FvLzd542TJW1ruKhCVrVRJNyY5KQy4mv7U04WkuNZwwuTgjxVrOqW87IjQjAqIsZFRk/FaBxYSz+RbA1Tjhc6bMTOszvKuL01QFv46rtTLJ6dURbKe85xVM5wTtB2JlhT47g3KzmvNXvjnEzBqrXEnUVGAuckIg6/x0WrcV5gjOON4wUIwbVhwiBLqFvD2yclQnou1uW8ZeuYl0sSFfPydo9UxdyfVewNczYHCe+dh1JlPwvihAs66lZzd1ZTqIjOC7YGirOlJleCRa25PQmQ2Ze3+ixqwzhPkDLQBL56d4pfZyI7/VACuznOSWLJC5OcNI6+q3T7/X5Gnm8cz+Oj4uNkPr/5iV/FD1k8zbLAxzlXtn7abo1l2RoiIShUjIxASMH1YcbB4v3eUWcsF2XHl2+MePNkyXzV8fb5ip1+yqSXsDfO+eq755wtG2aV4fpGweuHS2IpUev5i0RFQfosQ0ZmHOAtd6cNQja0raNfKJaNYVq2aG2DTLrV4ATb/YQ0jvjyzQ3+n+8c0TmLdpahiphVHUPvOBOwPcoYZgptHA7H8byhl8a8d1qRJ4HWPa9ahBSopaCnJN4KnIsZFDGRkGjnuTur6SUxRRKTp5LXDhbB98h6doY5t7b71K1lXi4pshhJsAs/Ni1Na5B4ykaj1744kRCMMoWQgZTw7nnJRh7jRVB1Va1Gu2AhkQhJqYNkedEE8sMbpyVfuLXBRRkAramKuCg1SsJWprAiZlVrzsqOzV4AfU6GCdqBwPLmScm46BjkCZ+5NkBbx6RQ9LOESAoaYxkVnqYLFIntfsbmmjRdd5qqMxRJjIolvSTi7nmLxXNRam5tFuwMw/cWSVgCHi7dPo/n8UnHR24+P2gDtmc1nubT3cc5l4oksZRUjaFzYTjRec+k8BhC70hKyarRXFQdVWsx3nG8JiXvDDKSOGJZG87LjjSJSeOIPPEcTmuub6Tcv6gRHob9jEmRcla1dM6jokAd+M5hSRLJAIqUjrtnK7KIABRNY7IkaH+99ygFNzZy3j5Zsqgt2gMiorGBZq0iQZErThcNs0qjrWd3lNJ1jntnK3qZIpKeqjFMa0MiJa2puT7OOVkZJnlCpz2LpqNuOhIVkSvJrArMtapzKCEwPji1ni4qikShIonxDusu6QEZk17Em2dLKu1xFkzsSaPg6dNoi4oEG/2MSHruniyZ1xohgm9QWLQdzjk6HFkimfRSEiX4jTdP6SUxURxRZBHMHCqOccKjtaXpPLujiPNVy7QK/kW7o5zNXs6PXJOcVR2jJCKRks1BghCCWxs5b5yWGOOwa3p3Y2HVabqZ42BaM6s7PrPd8MJmwd4w4+2zklxFjNM44JFOVnxue/A9l4+fU6Ofx9OIJ1G7bQN/HvgCARIKgPf+D38C1/U8HorLuZ2v789QUpAlMb1UMq00ryDQxnG8qDldtQgvmPQThPO8c17ymZ0+L+/A/sWKd86XOO/57O6IREXE2rM/W1017rdHPTpjqDrLII3Z7af8yI0Rh/N2/fSdop2j7CzLWrOxVVDkEWksmZaaNIsYp5JMSt44WnI8ryk7Q9caxkVC02pEHCTESniM84xzxcmq4WDWgBccLysEnlzFjArFpAiKszQWzEpNHnlmTUcvkSgJtbDMKocnKKykB+sM/SJFN47GWI4WDXcmkrm2vLiVYn3EvNR8c3/OKJEIL8hisBI6A60DHTs2Bxll6xDecjCriJUkS9R6QBPi2NGPIprIs2phmCVcW9On69ZQG83NUcaqc+Qq5mTZMcgivBAUiaSxjtpYro9zUqWQCEpt+eyNEdHpEmuD8GPgFb//M1t8+2CB1pqzsmWYxix12LTfPVnRy2I2eik3Nwq0c5yvWrSxZEqGbElb4kgyjCRO8j2Vj59To5/H04onUbv9TwTr658F/m0CzPP0k7io5/HoSJOIV3cHLNbunSoO9tZeAAKs8zgXEP0ASRIhAG0sm33FwdQzKzWtdaTRCuM9gzTGOUc/k6w6yXZPcdF4kkiEKfxRxrwyHM5rDhYN0luiSGGdDe6ZxmEBJQXXhymjXkzVWlYGzsqWlTbr/khEbQyltmzECkvYOJWURBG0OpS6Vp2h0x4kxBgOF5ZcKaSQWG+Z9CRZnLFatJytAl8OD154VnWHAyIBwzzBu2CBEAnJOE0AyY1xQmcD0sZ5z7xsESQUKqLNFGWrSde/v8aEDWezlzIuMqwrsR30EoGUMdOyQZqIcQa7w4xGe4Z5gIROCsWytYBjXlsao7HArY2cURYsriMZ0Y8jNnKFNp5ECjZ6MWUjOJzVnC9b+pnCEQGeRa0Z5zFvWNgoFFGk6CWSprNY75lVmkxFV9lpt5ZWLxpDocIsmAA6GwZNH2ef/ah4To1+Hk8znmTz2fTe/xUhxC94738V+FUhxK9+Uhf2rMeDpQcIKjMICjbrfVAaPeRG+vAxDx9/ecxlFlJqQ0/F9DJ19XqRKvI0wlmPjAT4gEhx1rFdJFjvKOIY7T2dtkz6Ma8fL1k1muNFy+2tHmkseftkiV434zeK4NUDDmMFRntiAVki6CcR37o/JYli9voJb52taLWmtZYskqw6R5oEnM2dSU6iYpzXnK86Rmu0jsCyqoPNQibAaANeUnuYTGK08czqliwOPSQpoW6hAcCTDBxq/fS+PQjImLNVQ6cdUnhqHajWRerZyBJWnaF1hs5GCAGpFDhvsd4GCXmnmZWa40WNEBIlIoQIduF4sDikkIxzSbr2pjmYLjHWUmmNczHWB0vxSS9FRhGb/YJIeu5d1MwaTdUlbBQp2gqMs2Rx8BbKVcQgS7g29JyvOt6+WNJpz6RI+NE7ExrjgjfPrERbwuYsBAfTBucdi9pxYyOjaj1Hi5r7s5adfoo24f5bNpZV5/jMVsGigeujnB+djPnWwZx3zkquj3O+fHNMvN5gn6R8rK2jM5ZUfXxjuOclumcrnqX/H0+y+ej1n4dCiJ8l+OncfMz3/66NB0sP2obFIkz0B2S+845Va5kUCTc3Cm5vBeujq2PCioyKJNo4OhtmR2alppfEzJuW1w4WlK1hmCv+8KvX+MqdzbUltuT/e/PiarN7ebvH0aLmV79zQhoHscG4UNTaE0vPW8clVjicFXg806pDCslKu9Cv0JZVo6mMpdEecMxXmmEeEccRh+cl57Xh5c2CiyYwwqZli3VhGPXmJOHasMc4j+is5+h8xemqZV4ZMgXLJvRXKgP4kJV0zqMQKCxvHpcY62g1CK/RBrQNIEwlIZagYkE/jUhVzPGiA6AzPoggiPDeBqGEhyKPwvcmMWfLZs0b06y0pF5obk96vHWywgFSSjCGs7Jl0XRoEwzyhmnMsrXMtGfeNkz+f/bePdayND3v+n23ddvXc07VqWtXV8+lx87M2A6eCZaRUCAoBqQoBAGJBIr/iBQgEQiEII5AAkISJYCCwCCkCKI4QQSsiIAT4YADmItiZrCjjD0euz3d0z1d11Pntq/r8q3vwh/fOtVV1dWXmqkeV0+fRzo6u9bea++111m13u993+d9Hh+SW6mSNLVnWaf/ClUO40wTA9xa1OxWmht7FSd1xzQ3eGCSSepOUDuH0qksuGqT2kDd9qw6S+ugsZ79Rc2PffoC3qdM7nhtaZzj8Kil84EL4z0muebWoqZUEhEimUimdjuVQSiJ95GDRYMicmla0rrA4arlyqSgcZ4vXp0lGaTv4Jq/t2g4WHWc1pbLsxItxfv2id6vRPci3QQ/KXjRSqbPEnz+tBBiBvwbJC+dKfCvfyRH9QLj0dLDmR7a4brj2k7JqrXcXzQopbg8zbEhclJ3qNP0HzTTEiklh+uWGOGl3YqDVcP9ZUemBONCc/tow6/eXpBniivzisY6/u43D7kwyfnU7pjXH2y4sVOilOT2Sc23T7Ys6p79SZHM0YCv313xyl7Fa4c1u2VGmSvuLWruL1p2qgyjJZmUbL3nzqIDIbG9IyDYdJauC7ioyU3kZN0iJJyODMt1z3GdpPiTPXVEnbbsljk2kyiZdOda13C07hlehhKgNOihPGgtNNYxHQmMAI/AyEgfYIipOCCkGI0A2j7d8L33RJnsEwojcCFycWxoA2RS4FykMIr9acmq7rBesO4cuRaURnHrNM3DEAKHa0fwERs8be8pjeDytOL+qmXVREY5ZEawaCydDeSZJNMQA8QIpU6lufkoUd8b51HWs1uVuBhpGkun0jxRkWuIkXvLlsN1w964oBn8ecaZJDOSB6uWW6c1N/dGXJqX3BrM/ZwHLdLM0t604PIkTwoHRPKtTZJGOmV5Wkm63vHpS2NyZXjraIOUgouTnGlhOG16ZlX2TDf8s2s+05IbexX3Fg1vH9dcG2Ry3svE8L1KdGflwBflJvhJwItYMn2W4POVGOMSWAL/yEd0PC88HlUlOMs+3vHsFgghUq9BK6wPENMEuVaSKk+NcyEEQgyluijwMQCpVm9DoI8w1gqloMw0jfXUTc+2T9nVtEjyKJlRbLaOznouzyvWXU+MglxZssHkK8R0gLMyZ2MjQURa16OVoBIaqQS1C2ybgDEKQSRKWDWOmUh9AYTg/knNxgXqFmYjhRCBXsW0sl5bbIyMM8NJZ1ltO7RMg66S1MA3gDRp8DQA2kAk0rhkqJYVks4H6jaQi3RhKp2OfdP2yfbARy5OC7o+MCkMR+sWIQV5ISmNZj5SOJeGb3/lzSOEEsgY6fvA/dOO+STHnDb0ziPwRJ8m/o0CYqTuoO5q6h6MTsHPu4DtITNgbUBI0Do5mJ7UoJRHyaSwvTfJOFn3jPYUMkrmRcbFSVJ9WHQ9WiommWI19Gcam5h0NkRMjPQOFlvLbWRi5fWeuk3svd1ZwWxkyIgsu0BFRArJl17ZIwZ466QmEpkWyQ21sRGV+ZTdxUjv0kBxiPGZ6dSPXvNawct7I9Ztz7X3kcl5LyWQM7moF+km+EnAi2i09yzB5+8KId4kkQ7+hxjj6Ud0TC80HlUlOCs3nK3wIdGM5TBrE0UaCsmMQj0iZxJjJMZUdkNElEj1d+sDmUyimL3zielkHZkSKC052nScbnvqznNlVtC7QKYFeZZUAprOc/d0y6b3GEGS6JERFyMxBC6MNF+8PmXbeY43KYu5u2ixNvnJdDHi+mRv3Dvouh5HpDCaMje4tqfB44PHunQ+pBDkCt48XDMrCpresrUpgxnl72QyisF1dDhflSbNx8SA957WKxoXkAqkStmR7SETIAvJvDK0Fo7XPSF6IuJh+UhJSZlLKiE5CZ5CS4SUKNJgaAzQeNjBs+1alo1DERlXGReqjOONpbGRzieqdcc7GdfZrTUAnRsWGSJlcVKAD57g06tb69l0Pd8+DuxNcsZCcdp6nPPcXVh2S4PWmk9Nktr42EhOWk8IgUUdiQFeP4BLny2YlhpjFFWUvHyhTNdC64m54He+PGNe5NTWcbixfPbymHGhuL1IauG5SRYNyzYyKSXLrcdoweG6ZWdknlmN40kljhCTFJRR7+0H9F5KIMALdxP8JOBFFGz9cG5SQIzxs8C/A3we+BUhxN8SQvwLH9mRvaA4UyXofaTpPXvj5NPS2ECuFdd3R1yaZqw7RyYFu1Wivl6Zl2kf65mXGTujjM4l6vJn9kdDAHFc2x3z469epMo0d0+3uBD4Bz99gcKkAcofuj4jxMi3jrZUmeLV/SmfuzThzumWb59uONz2XJ1XaKMojWJrU5/m0/tTft+PXOfKbMKkyCkzTdcHxnkSxdRaULcObQQugpCwtREF1J3D+8hOoZlV4Pr0kxvIFRxuLbbzIJKxXOfBR3BDGc2TXjsrDbmRVKVif15S5ImQcLyJOO/o3VCaA2YVTPOUcUQfCD5CdPTOsu0D3iXnzs9cmbA/yymMYNU7MhHYNj3OOdrOURiBTk4PHG+TovfaOjySMkuSPOuuxw+ltJ60kAik4z77t5FQmpT1GA1aQZ6RbvRCUNvASdMxrhITcFV3jDLNNNeUmUGSMphM8dAHKS80k0yjlGKUGS7OCl7aKThYpazo8qykyiS9j5QmqTbMS51IBZ0jM4rPXRpzfbfilQtjxrnmpXnFD12bU2aGB6uWzkYuTDIg9Vbkd3CzefSa33ZumMl6f1r2e+3zpFzUi3AT/CTgO/kbftR4VifTrwJfFUL8WeAvAD8D/DcfxYG9yHhSlQA+HNvt0X1CiNiQMh0pBa8+wXY7/YGOt482mEyRD5mULA2zKuNLr+xyuum4sTui0Iq3Trb8E5+/zP/9+gmTXLPuHbPCsGx6ft8PX2Fe5YyMRmtJ7wO1dXzt9glvHW2otKacKOb5lF+/u2RWGrS0+AjWBaosp/OJfjwqDR7BInZUGXQu0sfIquvRimEWRzItoLNQGYVQINs0M3RhUrBoehrrqLRhtU19ISHBGE0eHbWHaFOPAwGCyLaBdWsxJrlxFkbROU/XO1xvuDjOOa47to3lzspiXQokeCgFlApilsp/ApgWCqUETWux1tI6T5ULGh/p28f/1hrIdApG84lhUfdoUpDMMolAoGOgsT2jTBJ9oNSGTdOzaHvKQhFjMqW7vdgmMgEBIxTjUvPF62OONi3LLjHJlrXnc3OFEIIr84rLs4reO05qyyjTZFrT+8DrB2tuXhxhXUDJliJTjEtDoRQb67kyyZFEjBRUhUGS7MvjE2aC3+k1/90Yu52rVv/24EUTbH2WIdMp8AeAPwR8GvgbfILN5J6kqOaPCDK+10k92+eDWCchRLY2cHlnhAuRu4ua26cN19skheN9YNE4ynVH5zz3lh1Kp6xqZ5xxIS+Y5KmJvTcqGA9UbQAzzNcIJC/tjSiUovGetx+suTAp8AQybVg2HfMyozAQWkHTO0a5TmZsPuBcj4skORiRmv21DWxsCihKp4vdBk+mYNk4XGxwPtJZz7fsir5PAaGPcLhyaAFduk/jYkSclf9ITDbvoJeROPSsts5zZ9nyreMaLSJ1l7TccpV6MgpQInJpXnHa9EmEVEnyQuFsoIswEoK9cc667rE24p74m/WAdmAluD7R2TvnafrIog7sjaF2gs7DnXWXhmarJNJpJKwbx/1lQ2c9tg+sWkdhFDcvFzgPm95xtO2Z5imzvb5T8tZRzWf3Uy8uBpBKkOkUvKe55vXDLZD0867OCu6vWj6zP2ZvlLNqHZ11VLnk2rzktOnxIaC0wvZJs+87zTK+E1WPp+3zot0EP0l4kXT3niXz+RrwPwJ/Ksb4Sx/R8Xzf44NYJyFEWudxIZAbzdGqxSjJbmmwwfPto+Rnc21eUmjF3VXN8aalzNNK+rfurriyU6JFwV6VcbjuOKl79ic5RkucC7TWIaXg+k7FwbJNdg5GcvNiyetHdTI1s+CDpcoMozxDqojzgQj0fWDdRayHwsDeRBOQ1F1PqRXzUcay6an7QJUrYoDWeraNR6gkB1O3iYSQGej7VOIaZRBscufsA7g0P0plUtnrbD+pYF5JLpY5a9ujRGRrHSeb+A5F+4xdF2G5tVS5ToOzHuptT5TJz+hTF8ccbXq+ZZfkBvok8vxYEHqoqisFfUzK3pWAce7pnCeTETVovq26NPg6LXOElJzWlreOai5OMpQkkU8ETMqMiTF8+3RLLkmacCoRB169PGZcGuymZ+MdL88qjuqOw3XL7njKrNLsjTOqTLNsk1uoUZL9SY4U0GrJpVFBAG4UhrvLltsnNbcifOHa7LmoV3+3eJFuguf47cGzBJ9PxRjfU91aCPHTMcZ/5Tkc08caHzS/cDaoZwYrbCkFwSWp+7bz3F02RB852HTsjzNOtx23Tho6F7g4zrg8LZgWmhAj3z7Zcri2nNSWizrnpd0RQsDlScZOmXHj4ogq06way1ff3DDJFevOc7rteP3BJmUmLpXE1Cw1wQlJbWCUC/BpjsmHnmlVooncWliKXFNbR66g7aG1qQ+yW2om45xKKTKtONk09H3SIJOSNEfkUt+jJlGwMwVNP1Cyh56I0DDOBMFFDmsekjPqPtAAxid31m3fs20dVaYQIV2aPYkKnQHSpODIIMRau0hjLUFECmXwQXC4aWlsREuDlp5Kk+jgfRpyHUuYlCCVRotIqSVSCdatR+t0PpsQiDFlgEpIfBSURTLL8xGQkcooTnpPoQ0b57h71JBlHZNSs2oV80yy7AKbLtX9Xtkbc89btEh6fXtVzhsPtmgh0Foxm0hOt5bLs5zr8xIh4GDVcWVecGlakCnJnUWDEHCyTezHQOp9nbPLzvEi4Fn8fD7ItfQf+i6P5WOPDyqnnQ3qvX1Ss+0ce+McowTzMqOznr9/e4EUkGnF7sjw5vGWX37rFCUkVSE5WLd84+6Sl/ZKbJ88WECgZaIazzKFjyWlUQSZavu3T2teP9jw9vGGqlBkSrG1qYR2sGrpXeC37i2ZlkkOZpRrtplOpAMdybWhyAUS2NoA0TMrDISMB2uLktA6h1KKlRW4Tc9kLyfvIyOjOXEdGkHtkgqBlFBkqQ+DSIGl0ikD2htJ7q2SH5GPit47JKn0thhkfHIJ+5MkkHm07rA+zeJkmWbc93Q2ZS0ugPYgpKHMNDZCriOnPn3HSBrkXDfJPE+owM2LEx4sa5aNp8pgpARKilSq0mKY5Qn0XUh/k0xxZ2FRHtDgPSifHEyjh7vLlmmZMc8NXQjUNvk07VYGLyO5Tr2tS5OCW4uWGAMuwl41aMBlyT/Ix2Rn8erlCRfGyabh7qLDuXSdfe7KhEluWLc9L+1U5CaZEwoYXF2hzA2d8yxbx56U5+yyc/y245kIB+d4b3yYctr9ZYuWgipTNDbZHlwYZ4QYubtsUEIwKQ3OJ4WECFyd56wbTwxJDl+QHCwJqe9T5ZKdkUkMFhfYn+RpxsgFvnFnxaVpxq3TLSFEjlYdoyLjraM1X3xpTqEFp9vEmjJGs1MGvrWuQSRpnllhKHPB6aaj1opJLhFCcm/RYD3DsUimRVJDcMHTu8DJusPHwKr1rFsILmJJpbWC1B/yOibpm1yxoxXjwjAuNNOiZ9EGtIRGQRSOrkvsOU0qzenERSDLJNKlmSTnUyCb6ESJhhTkWtvT255RIelDkiLqHEiZCCKFgulOwbbz7JQ5O1XGt4+3BO9Y24AWScvNRyjyRC8+rQNGw6Zx4JMYaa7AhtTDciEiCfTD4Gs2ytm2lkwJ9kaGm/tjapt8lLo+sKgd+MCk1JRGkGWG1jn2RjlKShZNDwiuzEquzZNK9Y9emnBc9yjS9y+1eoz+LKXgwiTnreMt1geE81yaFnS9J4rv3rX0HOf4bnEefJ4TPmiI653nJVpJXt4bse0c13dKmt4PQ6PyIQ9/3VpCCHgf6UOgMBohk3PpuNBMyxTQ9sY5u5VJitBAYTQXxjneB+6cNjxYt7z5YEXrAqvGcX1esmx6fuVbJ4xLw/EmlX6WTUvbBRrvaHvHtoW2txTWMcky9mc5D5YtrfMQIpKIVKBFSFI+Id10hXfc7T27pcbFZMMdSMFCkno5SkmyGJgVmigk01JzYVxwYZShlKTpHd8+2tL1nmlhsFlk0zg6lzKa462j0DBWmkt7BQ+WDTYk7TgtBdok4UxEku2pOzhqArmEIpd457ACgncUoxFH6y6pBXhPrgVaCExZcGluWLeWZd0jBLy6XyGExIU6nesiSdsgIERBoT3WpxKhCzAb58ikCcrVecXaejIJAsG1Wc7N/RFvPNhy97TBDezCSZFTZZJxYXh1f8yq88xKTaEVUsLt04bTbc+nLo65MtMcbTo2raOrDNd3Hi+lKZGy4llh2PSeTdOjlOTq7OmqBOc4x/cSzzP4fKKv5g8a4jp7Pg7WxtZ5MiMfarz5GJlksOwcjfX4AEYqXrk45tfvrth2PbXteWVvhpKKnUJxZ7DHzozmd72yx/1VS25kynwE7I0z3j7eEELgcNOhpOTWomFWKJa1ZXcwiWusZ9V6lpuWlU1K05lKN1XfBYoqzQBdmBRJQUELDgYpn6UFGSO5SbMyzgMxoKXDByjzJBIqBhmIzKS+iA2eZesJQAyeEDuWjWOSa25eqLinWgLJj6ceONJaJzKC7VN/aOM8V4RkVhUIBV2/TUOqPrJTau5vLTqmno330HmQBKKUaAQnG4sQhtkoo+4c66bFlAU/cn3OUd0hhEQJTT7QyGsX2bYdUURiEFydFUghubWocSEQHMwqgxSKIleMjUQKWHWel/dGfGqUcbBqBqWGnGXtEAKKTPLq5REn255MKRa148aepioMs1HOONe8drCGmJhi+5OMB6uWG3sj9ic5s8Jwc3f0UCw0hNSrO1i1XJ0XnNY9We9wIfIj1+dU+fma8xy//Xjmq1AIMYoxbp/y1H/6HI7nY4sPstd+9PlRrjjeWHaqVHbZHWUcbjoerC0xRsa5Ylxk3Fs2rNvAzYslbZdu3GOT0UfP2wvLrNDsTRIJoco117Xk/rJ9WIIb54o3DrdsXCQQKbVAC8n+rOLanmScGapC8/dvLVjbnqazuAC2jxRZUlO2LjG6lBRsWkehIxdmJU3n0vyPTz2WbQdKpaAQI9RtoCokjUt9DE3a7gPU1jHONbuV4Whj6SO0VpAryRtHW26d1CzrNH8TYxIazTSM8pTtxQDBQRM8X7+3YFYYqkxS5hotBX2ILJqeXBlUluwemt7Tbj2th4kWacjXR6z3xKgIIdD2ka3uOaoFUkpWreV062ht0mBbdw2VGYZGQ2TVB17Zr5KhXe+QmWZ/muGi4MaFMY11dH1ahARg3fScbCxXd0ZEIRB4tq1nb5RxvOn53KUJy9piXeoN/vC1OYVRrDvHlWlBmev0N/GBt49r1k1PphXXd6uHgees72id52DVcWOv4tq8xMdEcTfDrNfHheJ8LkD6/YtnmfP5ceC/AsbADSHEDwP/YozxjxMrNWsAACAASURBVAHEGP/yR3KEHyN80PzCo89fmznuLBu8D3z97pJLk5xPXRzR9Z67i5b9QrPpPAeLhlXbc32vYjcr+NLNHW6d1ixqi9GKuvPcOqn57KXJw/fvfeDuomFkkreO7T1KJJo1MiJCYJJnxBjxUfDpiyNun9Yc+4Bt+pQluMCsNIwKg5HDPA+BKs+4fbzlpOmREcpSE73ntE6yPELDrEyEgL6PcBaQRMo+SgGKSKYUrYuMckNtA1oLDjcWgqdHU5US2UNthwAUoSRlL9YnxQSTpSBUDxP/hTZEI/jyzTnBRb5xsKbve5ZdxDuPUlBomBQK7wOVltTWs2h61rVDqciDpWfdeC5NNcutT8y/QpNJuL/q0EowVpImRhabDhOS3tzESJCKVWsxpHM9zTNMGbm1bFhue6oi2SkIIsvG0tpIVShKbXjtYIMPgb1pydhI6j5yb9Xwmf3pkDWKVLqUAh0F1wdRzyctO876jrkxnNaWe4uGl/dGD2en7iwagI+FoOeLpsJ8jueLZ8l8/hPgJ4CfA4gxfk0I8Q9/JEf1McYHzS9IKWg7z6/eWSIFOB+5t2w4rS1XZyW7o2xwobQPbxhGK6wNaCU4WLWcrG26IWpFiJEH646bF0bkUqXPjylDeLDtkAqiSKvdxllKo7m/6ri6W7JoPErAONcEL8iNIncB2/t0w4uRUa7YKw1femXGL72xZNX2tD5idJrPaVuHUMn6IMtARIHSirmSXJxqvn3UUlRDI14GemCsNVoJMq2SVbbt0UgKDcFL1n1P7wJaKyotsL1DePAyUreJTl1KkD41+X0IzEeGSW4QEl67v+HiKGOSa+60PZvOEYHdEnaKAm3ASI3WikXTcnRc4wMUw2DQunMs6sTWQwhETBYO284R8TS9I1eKGOEIS54pJvOCTCWiRxrE9eyNcnZGGVJKWpf6gTtVUp9ebHpq6ykyxdGqpfewaBw3L0rWbSDTcLju2J9YcqPZn+Q8WHe0tsfHyKVp8Vjggcf7jiFELoxz7p40rNs+ERFiIoiclYVfZMr1i6jCfI7ni2eV17klHmfJ+Od7ON//CCFyd5GYbaNCc+tkS9sHRoPr6INVm252PpApyU6VoWRqymdasel67q9bxlZjtGJWanxIfRElBFrLhxI/h4uWi1WBm3tuHYPOZLpRlpo3j2qu7VU8WHRUmSAzkigzMmOY5I5N2zPNDZMs2Rd84+6WWWlwITJxKSPo+kR/rrI0CKqVIKJQAnbGOY315FoOemeB28cdZQZXdwpWjePuac1OpZNdwNoTYyQ3MC81TR+o+6TKPMklcfAeylVSLugdOAnRp3LkxUnO6caBgkymC3PTeQoFL80UrY+UxtA6z7aOTKrI5VxzaZ5he0ttk1SQ90n40gdonMMMbLe19WgF1kZq64jBMR7OZ2U0i61l03l+4NKUm/sTDlZpeHdnnBQRqpB6cNYFFk2fqNGdY9v0GKP4wrUJ29pyuOl5ea8ayBmRB8uOH31lRJVr9oE3jzYcbSwHq479Sc7LF0YPs4GzvuKm7Tmt+yRuK+HSNGUMdxYNemDDveiCni+iCvM5ni+eJfjcGkpvUQiRAf8q8BsfzWF9/8IPOl+ZkWnafQgsPiYbbBfgB69O+ebBhtYFQoR5oVPfIEa2XeTKtGDrAr0LvPFgQ1JAW2CU5AvXklnYrDQcNZYuONa1IzdAFFweZ9Q+sbM6m2ZN3jxqmBSSbhvZGWmqTDEfa6KPLNvAxkYWtaMsDZvagkwkA2VAeaiMQBtN33smhUrT97nhsHUE4GTdkQT9STYQjWdaKk5rgRwM9UYZaK3YNI6jVZduMgrKTBGlYVIpllvLae2Qflj1DFYHuyOJMYqXdtPEf66TPxFDECnynE3XclB3FFoghGRSpkCwUxoOMoMQlpM6DahCCmAupJLh0cZhlOalnYL7yzQ42wfY9AG37CBGfuilHUad58puyazKyLTgW4dbTrc2mfx5jzGSTed59eKE+Tjj5bbia3eWKGBSGiZFxpuHG5QQzCrDxXFBEDzUCjxYtaxbx6xMTqKLxqJOBS/tVA+zoP1Jzt97+xQlUiZ7uTSc1j3X5/qFUzV+P7yIKszneL740KrWwL8E/HHgGnAb+JHh3+8JIUQhhPiqEOJrQohfF0L8+8P2XSHELwghvjn83nlknz8phHhdCPGaEOInHtn+o0KIXxue+8/EkIIJIXIhxH8/bP+KEOLmI/v85PAZ3xRC/OQzfNfnhhDikCV4QogP6a87pSGKxDQTCL54bcalWcH1nZK9Uc4Xr8/43KUxV2cF7dC0LpRgWmpe2huxOzKUmeBo03FtnnNxUlBoydfvLLHWs7Wez1+esjsq2J/mafZESLxUlJlIkvtNx71Vi/WeIjN8ar9inGnGmaBreha1S2ZnueK47jk43bLpLLaPSKlQQFkIlBbMS0ORKeZVRm40nfWcdj2lSWWepks0ZEIk15Jl45lkhpHJKI2gD8kuYdl4nI+MCkMMSVBUS0GhNVKqobwnMEN2oxUYZbh/0vDW0ZKDRUMg4qKgcSHJz6xqVnWg9SS179ywbT1Nn5QJLk8K5mVBaRKxochTj0oAWqfAVmVQ5ZJRlsptzif5H+89jQ/cPa3JjWTTeu6e1oQAN/cq5lWS1TFKISJcm+fDvJBgb5JzbVaAgEmhqYxiVmU01lNqTZkl59rDdUfvA/2giHFG1+99TIaCx1vePqlp+0QouDwruLFXcXVeMioMIUai4IVTNX4/vIgqzOd4vngWhYMj4J9/xvfvgH80xrgRQhjg/xFC/DzwTwP/W4zxzwkhfgr4KeBPCCF+B0m49PPAVeDvCCFejTF64L8E/ijw/wL/M/CPAz8P/BHgNMb4GSHEHwL+PPAHhRC7wL8LfImk3vIrQoif+176ELW959vHWx6skmDYWZnkjPW2U2VURiWZe5E8YXZGhtuLhhAjVW748isjauv4tVsLfuuwZtVYbuxU5Jni/rJlWfcsO8+49GRGsWx7ti5NWV7frXj9cMNnLk3JtOL+puNk02F9YJor7p6k4UujFLZ3HG88q8YSQmr+hyELKDKNEpLe9ygpsLZnVmqEkBSZgiiBNDy7bnuO1i2jQpFJkvyM9eSZ4MJY4YJg23tChJ1RxsG6w2iBcwEtko11EKlHNMozVq1NwdIHLowNbdenrEdGCpMyyHXj2HQerWGcwa2jLQhBFIHOwnKwdchI9hCiiHRtpBwX5LliT0m2vaM0AqcBIo1NGV1mNMF7jrokDNoFQaYh1yARRCEIHmoXuTKv8CFyWrdMirRIOFi1w4o9ffbpBvanMBmCK8C4SJnvyno+vT9KVPsYOdx0fP7qjDNtEaMkISSNvRAjx5uOi5OcyRBg7i9brs9LtBwGTc88pIaMwRj5sRL0PBcg/f7Gs7Dd/kPgTwMN8LeBHwb+tRjje1oqDJI8m+GfZviJwO8Hfvew/WeAXwT+xLD9v4sxdsCbQojXgd8lhHgLmJ4Jmgoh/grwT5GCz+8H/r3hvf468J8PWdFPAL8QYzwZ9vkFUsD6ax/2O383CCFyb9FwurVMinSaF43FLCQ390bvsmTwMSIi3F40jzVZDzcd904aOh+5PC0Y54qv31kwqTKuzUquzAtONxaAGCLWB46WHS5GtITdccaqcexOcl6+MOY37y3QWnHrpCbKQNMLxoOF87JxKBnZqQq2nWbbW/oO1i5lHxemBaNMYpTAA/Mq48HGIYUnEjFKctT2iOBprOC0cSiSJI6RkhgFs0KjtKC1qV8UJVjrqG1glAuUSBlabXtClOQKtBRIAbUNSS1bKTYhYowg14pt5+h6MINP98Z6egfTItG7FenC64HjFpatp8jg1csFN+YVbxzV6fxLjQyRaSHJdMBIiTYSGVPwuLZb0nnB6VqwbCzGSPZGGUII5oXEOc+qcazanpf2IFOJPm59oLGeC5OcddcjJQgR2a0yLk1zbmYVSgpWbTK+G2eBq7MiBdkYCSGVoa7MS6wPPFh1uBAY5Zprw2CpRNA59zDDeT/K/8epZ/JxO95zfHg8S8/n98YY/y0hxB8gld3+WeD/4AP8fIQQCvgV4DPAfxFj/IoQ4lKM8R5AjPGeEGJ/ePk1UmZzhtvDtn54/OT2s31uDe/lhBBLYO/R7U/Z59Hj+6OkjIobN2687wl4FvgYHyuTQCIROJ9ERM1gc30GSZJ+ebLJamtL4x1GS5QUjHPDuDDMCs313YqLk5yv313x5uGa6/MRX7w+Iw5yPYHI6aZj1XkmhUGpZLFwYZpxeZZz77jjZFuz6BydiwQBVWZo+sAoN2x7TxeTRIHSkUmhsSEyz8zDVfrFccfB0ibvGp0cNGWILBpLaweNNWAyikgcaxspg+Qzl2bcXqYMoe8EV3c0nUuDorUNbD3QBTIBnau5MMkxOtlVHyx7HGCIrBiCGInwYL1/KFRqPclPaPDxOVNaECQ5nIPlhqZ1nDQtnRdM8uQT1NseozTjXCEQPNh2iT24shTGEIWgyDNCCHQ+ksnItk2W4nuTAqOSaN23jjasm543D7bsjDNETJnIg1VL7yNvHqaZpms7JVWu8T6Qa83vuDrjtLZsO8ctF7g4ybm9aLg8K/js/oSbe6PEBlsluSbguWY457M15/he4FmCjxl+/5PAX4sxnogP0fwbSmY/IoSYA39DCPGF93n5094wvs/273SfR4/vLwJ/EeBLX/rSB4mnfmgoIR4rkwyfhVbyPZumT2uyZkZRKs2i7cmGVXSmUjOZCGWm+dylEW0fublXcby1HKw7VrUd3k+yaVrGRuJIZa3jTfKP+fz1Kf/Xb7YUyjOaZdR9cgE9bnpyKdJQai5Y2Uht4a2jmnEhKLWC6HmwaKl9wBFp+0AIjs56QoDtmVI1iYa92AYEUGRJDTvILXuV4vI0NcuNgNfuLzEqWV6fQcaknH37uMMMhnBnQaR74vyt6ndsukdJ/i7puJHUspVPj3cqqHLDqoMQWtatR8rI2gtcnzT19jLJqnU0vUtDpSKytZbTbcf+rGRaCrZdRIqYzP5wfOtow0vzguON5cHGIgVcHOU4Im8db3nt/ooLk5y6C1ycZNy4MOLVyxOWdU/vPLMy58ZuidGSi+OcSaGZFobMPE41zgd225V5+dwznPPZmnN8r/AswedvCiF+k1R2+2NCiIsk1fkPhRjjQgjxi6TS14EQ4sqQ9VwBHgwvuw289Mhu14G7w/brT9n+6D63hRAamAEnw/bf/cQ+v/hhj/e7hZSPl0kg9Xyuzt9bV+uMrXR30RBjmk25NC3YrTK+dnvB4bpDCsEP39ghN4plnXo7FyclnfP8xv01Ari72PLGgw2lUexPSwqTlA8u72QsGsuythRGJZtrAk0PWgXqznO0tXQ+orOI9ZL5OMdte5zzLGqPdbBtV1SZ4oCOSa7ofaJB+xCxLgWPQAo8HqiHnstMk+R/ZOT+oma5FuzNA6/sVpx0jo31nCZXh4ewJB+fjGQo17p38/vPWDP1kGWNNIwGdYUspCDoh2OSJFO6dejREkxRgrdsukEqaDhuQQoeWiqmmeCw9qggaFzEhkjXBjKtEEKyNyk4WLe8udyw2FouzSoyIqvW4n2yrHAKNjaSKUk+kkgl8T7R1a331FuXAo1W7I0ylBQcLFsy895U40d7IiImksSZG+53gvPZmnN8L/EshIOfEkL8eWAVY/RCiC2p3/KeGAJUPwSeEvjHSISAnwN+Evhzw+//adjl54D/VgjxF0iEg88CXx0+by2E+DHgK8AfBn76kX1+Evgl4J8B/vcYYxRC/C/An32ESfd7gT/5Yb/v80Bh1MMyCfCuocAn0faeB4M76d3TBoRI8xzTnC+/vAuDGvHZyvdR6+7XDlbJmtt7fuPOigeblt1RRmcDR3XKhCYHSW5gUmi0Sn2VTCumpeSNww3eRfbHGRvrEN7Ti0Q5ti5QGUnnA7YH6yNGOuwwGDnPNUJJQnRolVQM2iFDeTSV7CMolwZNmw6KUaTrHK8/WFP3gXXtaJ44J4F0kSqSQOjTBssk7wSWSsPeOA15lhqCT7I/TTsImwKbfjCaA1qXFLrr4Y09KXNa1ImGHnBIER/K72hBUsnOUvtSCjjcWnxIw6jL2iIEzEtDJiUbGykzQRcEIkZO6o4Lo5zDRcODZcP1nRGL1rJTZhyYjhu7nl+7s2R/mvNg1XFFwLgw2EF8VjyRm0spsH14LtnK+WzNOb6XeBbCwR9+5PGjT/2V99ntCvAzQ99HAj8bY/xbQohfAn5WCPFHgLdJ/SNijL8uhPhZ4BskybA/PpTtAP5l4C+TVFZ+fvgB+K+BvzqQE05IbDmGsuB/APx/w+v+1Bn54HsJKcVjFtvvhbNVpxKwtZ7OB5QUjPKM08EM7OW90cPg5QYDukxKooDcKPYnOb92b8G1ecVpbTnY9Lxtm2Tn3Do2jUNqifeRTDt8EAQBx7VLmU3rGBlNEMl4LArPndOWpoO2i7izG5+HTZdKawJQwqEGhloyfks05EfLYoZET175d4JF0o5znNSecWbedWM9gybZcguXMqF3nTveUc2OwOHG0Q8uqEpB3afnpnnSoAsxBRkXobPvnjdww4936bgX2yTNo1X6e6y7js5GKiPoYlLzDjGRJaIQdMsOgUAqQegDO5VmlAk2raTuPHIqEVIQQuBgWVNmiVY9LTRvHG7Zn+SMMs3lKdw6rdkbZSwbx94oe9j7OQsuzzNbOZ+tOcf3Es9SdvvyI48L4PcAf4/3CT4xxl8FfudTth8P+z9tnz8D/JmnbP9l4F39ohhjyxC8nvLcXwL+0nsd34uERy0XeucfBng5NH37gaggESxqy9fvLPEhoqTgB65M6HrP2jpONz3dYJiWO8+dxqOjREmJgpTVkCRplIAsU2iSJ8zWerxLQ7DEQKkVbUg3a0e6mZ/1cZxPWYQBug6mI0HdRjKTso0nM5TA49syoPWRkQRroVfuMevqMxjg6lxxf+MfZkUCyHknSJyV6SKwdum9zzKYbvhQRQqY/fA6ESFPvIDEcn9K4HPD52caLo4zhBKcbnsiII2g5R3tOS0EQUoylUgRiyZZlwejmWYaqSW2j9RDefLyLGda5pzULdHD0bqjMIpZaZCqwPo0o+Rc4N6i4ebeiNkof1dweZ7ZygeJ434ScU6++OjwLGW3xyyyhRAz4K8+9yP6hOJs1dl2juNtz+E6MZnmlUGQyAtKpHmYr99ZUmhJkWkW247/9dfvMys0jQvMRgbhDQi4t2hwPjIxglXnMLlG+6Qq3XmPRuLawLyQIAQyQt0FkMm+wLaJPDDKoLbvZDIe2Ax3fEUyejvexIezNC7wrkByFnjy4beUqRRWSo+SsO0iTf/u85IcVNNrphn0NjUdB/NQAu8En7PfT8uOPI8HP0EKPLke3su9+5hhKNn1oKSkdYG6DxDgwlgzyhRv2xolk0dR13sscHla8dJexWcvTVlsLZNcc3m3ZH+ac7LtuTQrON32+BiwfTLVa/vA6abFBc8/UMw52nQ0Xc+6dbgYee3Bhi9cU1SZfiy4PO9s5Xy25h2cky8+WjyLwsGTqEk9mU8kzjxTQnhnyexc0lhzLjz1+Se3nf3buTS9Pis1d04bciOZFprCaO6ctkxKzYVJum3bkF6rlWRre147WPPN+2vePNqihOD6rGQ8UjStxQCv7o8RMq3QN40lCsFYSyZGc3muGRuSVbQSSAnakAYimxRw2sHATYsUaJ5EO/xY0vONTbYJ73neSO/lAxgNWWYoM/mwP/ToBalJM0LrGqJLem5nL/DDZ451SsOfBRnvUK8JqUR49rmPfsezx32At45aDpYW7xKLru/TTJUfbCJiiORaIaVEILA+sm59clWVScliXBg+d3mKkZJcKbyLGC1QQnJtXrA7KbixO2Ld9rxxsOG3HmyRAqosldPunNa01j0WXJ5UAuhcYG+cPeMZeRxSig/sT36/49Fy5ijXGCVSIArvURs+xzPjWXo+f5N3ihMK+EHgZz+Kg3rR8bQVUdv7h6WwECJ7k4xxbh4+Dzy2z7wyLOqepnfDRQ1Nn5rvl6clZab49MVxcjiVSWLlWFjGmeJkmxhrh+uON442FEYnK+5Fw9G6Y9tZvn53Te8cMiaiQKlFGnZVkk3j8AROjrpk/hYSsaAbrKijSBbU0UEXYCDVfaCK7NMyjifRk4gHAqiAputRKik9G5GeO4MD2jCUyh7JtM5mdYqBmpYr6Lt3Sm3vd3vIAClAxZR9jUsJdUAMfapHv6MneQkZQXJtVWmf1sLB2qK20PUwypNdtiOgGM5hSOSDL16f0fXpxJ5sLF+8XnJtXjIfZXzl9UO6pWc6aLWFGKltskcXMrJbGSZlxvGmY9X1NH0gBPihl+aPBYazbGVrHUfr7uG1cr5S/85xTr746PEsPZ//+JHHDvh2jPH2e734+xVPa/DeOa25u2gpjSQzirePtywf9Hz5lV0A7i6aJKqpJVoprEuB6qV5yaZ1bNrkaHmwaglREIgUWvJbD9ZcrHJ29rPkTjrQqT+1W/HNww13Fw1153j10gQJfPVbR/RAJSW5lJxYR29D6tMIiYuWKjM459jagBvoxyGkgcxMpT+sfYSp5kg34TNpivdJap4ZhR7mcKLHO6ifEjWeDGhn5bazTOVCqVg0/rG+z/vBkgKPAao8UcxtfOc7PwpBIk8IoHEpOJ1BAnmEPAMhJeNCs/WBa/OKl+YVZjD2++qbxwghEAJmheH2IuPHX7mAlIIoJEZKTjc9cUjFPnNhNIiMar5xd4VWglXb85mLFbkxXJkWLOqeaWHelZkcb+xwjb34lgkvOs7JFx89nqXn838KIS7xDvHgmx/NIb3YeOqKqLZY79kZZe9crMHT+0CZJWM14KF9sRSpKR1Fmkw3OvULIrBTaeq2Z1Jo2t5TFQohkj+PdYG3T7aUWfLxybVgtzLcXzTcWzYcbS2TPEOVgkik60K6uUswKuAtBNehpaDMNbV1BAcIKEzKCNomZRpDQvSw9DS87LkhAvebdAEWArbPUM04iwExwINVaviflew+THD0pO8VfDqGMxLCo8hIgcr6xz/zDIFUbtspJIXSSCWgF1ydVlyalxyuWzbWUQmJjIrWe0QI3D7c8hUheHV/TKYlP3h1xrrt2VrP4bLlxu6IrQuICDcvjPAxyeiUefbQGmHbvXsFfr5Sf744J1989HiWsts/B/xHpEFNAfy0EOLfjDH+9Y/o2F5IPG1FlBuVnDmteziNTkxzPWevE/DwcYiJpSYiA7styeqkkplnXmmq2jGpNNvO8/bxNs3ltD1N5yiN5MG64+2TBu99WkUTCR62tuXuqae2aQ7nTNvMh8Tq6mN6rRECo1JJqe0HGvQjDf+zm/hZGeq9JCO+Wzhg8x2W0XtSP0oAo8EC4UnVg/fbd2XT76d9r7PS3nuVEs/Ke6s6wMgzEZqdsaHKJevWU3eOiCAGQRCRTAmEUsxGGVLA6TZZpscIO6OcSRGojCIzCqkk37i7wvrApUnOq5cKLo7zh9fW01bg5yv1549z8sVHi2cpu/3bwJdjjA/g4QDp3yGJeX5i8LQV0bWdir1xztfvLNlazyjT7E0yOpduAFfnJcBj+3zh2oxF3TMuNFWruL/sKYzE9YG6dbxl11yel9zYLWn75PZ5Z9FydV7w2oMtIkb2Jjld5zjedFSlYlooGifIdVItUCTWWqYEnY84D5NMsG4jmenJpULkks73uKcwzZ7E8yy5PS+c0b9dfDZnw0gKPGePn0THO5RuTVLa7uLjtO5AImRUnedUWD69N6HuHJd3c2LMk14f0HWOIARaCpSESZFRZprLM8m6dUwzRZTpRhcjHKw6Ls1y9icFmU6zQS5E+u69V+DnK/WPBufCph8dniX4yLPAM+CY744t97HF01ZEhVH82Ct72BDIpHw4g/HoiunJfaaFwcfIzd0RbxxuuHVa8417yY9nYz0Hy45ffvOUl/ZGbK2j7XveOOyou555kTHJNCoEXqtbwlogpCBX8OmLY3ZGht+4vyF4x6K13Fv0jErFlZ2KbN1Rd57rFwoypXmtW7L5EMHnRUYXn9GW9wMggKlMNPI2QjEIlJ4NtOYkooOLkBlNLhVr6xg7zd4o5/Ik52DV8a2jDQhJ5zwXxznTMsOHwGnj2K0MVnu2LqRB2kqwO87wMTIpE1kFoPfxoSzT+63Az1fq5/g44Vn+v/7tQbLmzJLgD5J8dT6ReNqKSGuJfiQeS8RDOvXZzeBpqyijJLmWvH20TatsoyhD5HDTsjs23Dpp2BsbXICLY8P9dYcWPZuu5xt3l5xs+tRoFqmEJUSNjSUuRqQ0vLKjOV4vwXkOlxatJRemiiuTkrdOtg8pzMa/kw38duODWGtPw9PmdL5TRGA7pDln/Z1ZAbmWhBjQ8v9n781jbc3S8r7fu9b6xj2e8c73VlVXNT25IbghJBCBg2SsyAYnMTKJFHdsEiTLSoiURECiCMcIghOFP2xLjoiNDJYxEOMYHIFQG9sisZvGDTT0UHTX0FV153PPsM+evnGtlT/WPveeO59TdW/1HfYjHe1zv72/fb7vnrPXu973fd7nCfRbEU+WRHQTs1Am9xgl5Immk1o+fmbAajdhWllW84h549BKcWqY4axjNG/Z6CnGtWV8fcr1/YozwyxotGm5Kauj5ZY6+oOw3Kkv8bTgOISD/15E/mPgWwlrw0977//vx3ZlzwDuRcmOtQp2C61ja1LdfK6TGvLEMK0ts7Jlo5fQOMeoaGhtgwj0MsMHTw7o5wlvbE+5Ni4YpDGV89jWUbcO512QxKktw07CeF6zPQ1S/SjB+kA8mLeOz7xVB+fQxar9KBfv4+LOYPN+TFM8jKBwMFaUaUgiSJQsXEdjNvspk6KlqB0KCU6lSji9knFxZ0Y/jxgXlm/9wBpprKmto5voQMUnEE5q62mspagdgzTGHoq2KAAAIABJREFU45kULa0Lv8tx2bAzre8pq7PEEk87jlWp8N7/MvDLj+laninci5L99vaM2Cga57i2F/o3vTww5IrKcn6tw+lhwls7c9xigPGl9Q7zxrOSGW5MK4q6YZjHfGAlZ2tUcGNWMysaitbTtkGpuWxAa2FWObSG1ilOr2Zc2pmwOw82Bd2OIiJ06xse3s8xLNShH9P/16MINkdlux3goIT2oJ+tYKFgoOnnmtJCN45YyVJODhVXdguKukEr2OjEGKXZ7Me8tNHl9etTruyXKCW01qKVYr2bcGEtR0S4uDdjUrZYH9iHWgSlQyZ8sp9yeVRwYS0nNkuF6SWePRyH7fYfERSpN7lFBvLe+/5juranGndSX5USro1L1jsR46rlxrRkbzGE2EkjwHJ+NeOzb+9htGJcOz56ekBsDMNcsTcPsyBvbhcMc83VccG4aNif1oBnXDTEKqgAaB2yLnzDvAqKBfvTMCS5kim8eGrnmC+oXEdZsI+aFT3uIPUgPOw+7hWcHhb0FIEJKLWliRUrWcw3fWANEM4MMs6v5EyKhkllSSPNaFbzwnpOJ435tg9u8jtv7TCbt6SxYSUSLJ6roxIHJEbzkVMD3tqZc2VvzkY/ZSWNMYvsGIHY3E2dxrHs6yzx1OM4mc//CvwZ7/2rj+tiniXcSX2dzGuuj0PA8d4zrlpa6/n85X1e3uwutL48p4dpMCDzjs9fHpMnmvG8WQyjWqKO8NaNije3J1zcnbM1rbFtE+RiForSajE/VFW35mcOdguD2NFLhbIJqs2PusdzEKTU4utrWco7DMOtYVkFiArqCQ9DC+CgG0McqYUkj5DFmst7BRZY6yUYE1TmYqMYZMHivLKWRGvynsZ5mDee3XnBx8/26cSGlW5C3QbDwHd256RGMcijhVtrybVxiQI6aXSTOt20jsuHyrXLUtwSTyuOw1a7vgw8R8dhza1J2XB9UrPejYiUULVBKjoxirqxfOHKPmvdiCzWdOKI/SooEGx0E0bzhmvjOb/3zi5b+wVvXp8Rabi8O6eoGxLtiSKNbYPUS1mH2R57SFk64ZbdwMFMT9OEhfVxEgyelMBzAM2tzEc56Olb9rz3QgJ0o6D8cHol59xql0gLf3BxxKXdKQ7PRieiqBzDzHBqkPN1J/vMasukarDWs9qJmFcWQUgjBd4zLcI8mHOeNNJcWMv5pgsr/NsvrNFLA3khTwwb3ZjLo5JJ2dBYz2YvYWtSPVN6Y/fSQFzi+cBDM59FuQ3gsyLyi8A/4dAsn/f+Hz+ma3vqcUB9LVuLLKT7f//iHrOqJdaaFzYyRISqcXSTiFkdlsZmIUyaRhotQhZHDLKINNKMi4aLexZjNFoJzgfV58iEslovFaalJ1Hgo0APRoILqPWBGjypPcaERvpeEYLUo/7oP4kzQakKunVGhywRd//gGwFZBN1U472w0U8QHEXtsM6SpYpuloAI1sG8sWSJ4cxKRt16zq7kpEbTWMfevKVsLNbDSjdmc5Byoh/stg9mcs6tddBKwsyS89yYBKKK957VTswwi58KFYPjWBAsVaOfbxyl7PZnDn0/JziCHsADy+DzACglpEbTes/etGatm7A9runEkEaGlTRQpxHY6CVcHRU0NnwYV/KIS7tz9ucNWoTxvGW/bKiasABuT0vqNuzitYEsjTEK8tSRKGEgcH2/YbxIgQ400exCQLOon7zs5HGhJdhAZEAvDaZykwc0phogiYRBHoMXbkwrEm04MUgYFS25CYoW3dRQtw6lhDPDFOc9652YWIW+zalBxs6kXqgcKDZ6CUZrOrGhs2puW6id8wjBCiMswoKPPKN5wzCLH7mKwaP2qjlOMFladi/x0ODjvf+LR3kjEfkR7/3/8t4v6RnFIvPJY8OHT/UorWOYRhij+dCpHlv7JQisdWPWugmxUlyblDTWUbeeE72EG5OSS3sNsZKFC2lood/0f7OOlW5G6xzOQ1E3d9kb1EDtb/WAnjcUwKXZ0V5btp5+Zjg5yHlza8KkaXF4VvMksAtnNVuTkrpxrHUDOy1SiuiE4l+9uY1d2Cz0chNmuSKNUYrNXnJz0Y8Oze4oJax2Yt7emYEsWG+DDLsIEuHfj0bF4FFnHccNJk9DFrfE48WjHAr/XmAZfO4B6z2RUZxf64ThQSXMqpbTw4yytvzBpRFb45Kmdax3U1rryWLDRjfhxY0uO7OKt/cKRvOSYR7zkTNdPvXFLVayBN0RIuW4tFOSGkUWh9rSl66Og75cBN2FJ8/hjf6BLM3zjPtRsxWhRCcS1AWmZcPLmz0ujSuqquLqKAx+fvj0kG6q2Wk8690YpRTeBzabAJFRrHaCzFI3jTi3kmOdv22+6/CiXzaW3VmNiGCd40Q/xSgJ+nyL7OawioEs7C8O/qaOiseRdRw3mCy16JZ4lPI4y7+a++DgA1U39mZ5xSiFQfjSlTHTsmW9m+BFuLI/Z1yFuZGtScmlvYJ+GvPRUwPWOimT2vKFSyMEj9ZhWFUrRS83nBpmxEazO2+oFwSEcR3KTSVPjnrBk4L7/cF2DPQyoZsYjBaMURRNi/aeWEe03hMbQ+Mcu9MWpYTdWY11IaAUtQ3CsYsF9kCVHLgvYeAgIMRG8cJ6By2KS7sFVevuym6UCkOql0YFF3fnvLM7D9T6I+KwZTtwU+zW+ne/HTkcTICHBpM7TfAa65dadM8ZHmXm87xvpO+L2gb30cvjwNPY7CVcWO/Qek9lLZFRiAQ3Ue+DvcIBrTaLNUXdsjWp2J83OOu4UbbMW8e0bPDOM6uFNFZsjSuSSJhXgXo9dcsdwYNw+A9WcysztC20kSeLNdOy5fQgw1lQquXCaodx0bDejdmbNQwyQ92GDOSr21MGqUFEMBoyMTStxR5a6A+yA+d8mIey7uai3zpHpDWxUVxYC46mZ4YZyR3lsHeTuRzu7zyOrOPdCJsuteiebzxqLcYl7sDBQtFNDP3NiKoN/jOxVjjnSbRmXtVEKuiCHfj71G0ISv00oqxbJmXDRi+htg7vLfPKY7ynsGAUnOimbM9q2qahbT0HzNXljuD+uB8jzyuYFhDrhkGa89ZuQR5pWhe8mC6sd5gW7eL3BSvdQLdOjGaYJ/SziOvjkqppmFWW9W7CaicO1tQiTMuGvXlD3Vqch1OHVM+1CHGkWMkiYqNv6wkd4Lglrnv1dx6HAva7CSZLLbrnF48y+Pxfj/C9ngk45ylbi7VBW61Z7DCrxjIuQpnmwlrGvG7YnlbgHHmkghFZ0bDeTXlxI+fafsHbNyaIEurWUlvHtGxJjKC0IGiujStq19I0DiMGeyfTYIm7cLjno7k1GNtPAhXbeRdMArOEs6s5devw3tNJDGvdmA+d7PHiZgclitpaEhOo1s55jBbODjPSxCCem+6jm72E33tnDy1CEmlWsohr49AjOjVI2ZnVzKqGWdXyiQurN8u0hxf0h2Uuh18P3DdLehxZx53B5FEz6pZ4dnAceZ0N4L8EXjh8nvf+Ly0ef+JRX9zTjIPdZuscF/fmtC7MiBR1y6iomFaWSdGgRPHhU316ueHydsVXrs/ZL1qGmWGlEzKdWVmzX7S03rMzrtgvW6yFQR7Ulq1tcQRlg3kNsWrRh+tIS9yGmNvdSw0LO3EgNsFwr5eGnkwaaTY6CScHGZu9lKZt6aUx2ii6iWG/tMTKk5oQSLanNfOqZWdW89J6l2Qhj3PgPhqZwFjLIh0WahH2FzpHSaQDUUFp2jaU4+7HSrtf5nLn69e68X2zpEirx5p1LOd4lngQjpP5/Arw/xIM5JbL2gNwuCafGIMA72zPWO+nTIqaN67PSKLwXN22fO7iLm3T4lE0zjKZN+zOK1aLltE8eO/0UsXrW1P2ZkEsNE9gPA9EgoNde2zCY+OW5bYH4bDyA9yyDU8IJUyFp3GBTaYErHPkRhPHwoleB1FCFitircljRdPAx88N+MKVMUpCEDnZS7k2Lrmw1rm5+B5kIkaFUtrh7MV7z7X9gsRoIg2lwNZ+GTyajLora7lXietevaDtSegzvt+ssuUczxIPw3GCT+69/6HHdiXPEA7X5Bsb9L5WugkrmeHqrmd3XlM1jjzRWOvoLhSNG+9oaocywngShkmr1jKrWsYVCIIy4GwoC4lAtJjZUYB3txw2l8y2e0O4FXwaIJag/KCAQQa1A0QhCIM8Ik8MN6Y1n7u8zytNh/XzYUbn1StTWhcW9I+e7hNHmlP9lCQOqhS1dbyzM2dSNsRG3zbbc2fWcnqY0VjHlVGJSAgOp4YZVWPBhjIf3N3bubPEde9ekGOjl9ympvB+sMqWczxLPAzHCT7/j4j8B97759ZA7qg4XJM/2GEqAs12v6zYmZZEWpPokKWMy6DR1liF0cLerKG1Dms9WlnKxiGuZVp5bBtUqrWEXboBohiKKpTgqgde2RIHGWEEJAbaNgSkCKhtEGZtW0eJI49SytbRi8PvSgT+1Wvb7M5qXjnZ48JaB+88l/YKvv70EK3DsqqUYLxwZiXjzDC752zPnVlLrBVnVzJEgtq18x6nVZDbOWLWcr9e0P3UFB5nL2Y5x7PEw3CcOZ8fJASgQkTGIjIRkfHjurCnGYdnGIrGstZN2OwnfPnahKqBYRaRR5pR0dA4R+s8p1d6RCbQq42HyEAaCcNOGmi6WrPeT9joaVpg3gRl5iSGugHnbmdvLT/id+NAXmiQwGZPE2lBa8gkBJZZDaOF1l0WRVQWvPdULsyiTIqWPDEoLRRNMAPUKjASneKuuZXTw4xIq7tme66MitvcbSH8zQTWm1A0lsZ6Tg2zRVZ0tFmYB83OKBXUFA76Qu/szt/VjNBRsZzjWeJhOI6Tae9xXsizhrsm0fG8sN5hWjXsFTVN6+inikgrrk0qrHecGGbEJlBxe7HBodiblmzvl0QmQkSoJWQ4m4OE1jm+ul3StJDqoFbdEn6phiAls8TtiBXkiebcSsaNScNoVuEFqjbM9ygJ2c8wj0A8K2nMuLYkucZCsMiODf0kEAx6mcZ5IVYKY9RdGU1j3W3lp9Z5Lu8VtNYRG31bE/5+VOXjsNIeRnd+P3sxyzmeJR6EY1GtRWQFeAVID45573/rUV/Us4KDmnxjHeJvuYue7Ge8vjXGege15+wg5+p+xYmFykE/ifEIeE9sNOuDjMSEks7F3YI0ERKjsRVEAklyqxTXtGHxdC44li4J17fQTaCbaDa6CXvzliw2eO8ZzWu8gzSBWIRYC8M8ItGKyGhS54I/UmMXNOuEWdUwbyzWwodO927LYA73NA6Xn5QSru0XRFropRHO+7sW/nvNvRx3FuZBr3+/ezHLOZ4l7ofjUK3/C0Lp7SzwOeBbgE8D//7jubRnB03ruDouqSrL/rwmizRr3YRIC84FZYNx3TKpWxqvubCa0ctiRrMaZz0vreYYLXzx6j7W2YUHimde1SDBu6cVcBKCUSTQKlB2GXxugws05rNrOftzy7lhwtt7QcImMpYsMohSxEroZxFaadZ7CedXV0PWuVCc2OxlnBwMWe3ETKuWvVnNpAxafXdSiQ9P/tdVS916zq/lNxflwwv/+zETs+zFLPGk4DiZzw8C3wT8tvf+T4jIh4D/+fFc1rMDt2g2n+6nvHF9Qj+NUFp4Me/w5o05KMf2qGbeOEQc51czRBR7s5rdac32pCSNFL6B1gof2OgxKi3b4zlb02CI0AZSFAKkBiobfHued7q15paAai8OPbQWYTS/5bf6yuaAxgs745JYIEs0G72Ubzi3wodO9zm32qG1gaE4LhpODVLOrXToxIY3bkwZFUEI1Pugs/fKid5dgeOg/NRYR6QVZvH84YX//ZqJeTcyOEss8ThwHMJB6b0vAUQk8d7/EfB1DzpBRM6JyL8QkVdF5Isi8oOL46si8ikReW3xuHLonB8RkddF5Msi8l2Hjv9xEfn84rm/IRK2aiKSiMgvLo5/RkReOHTOJxc/4zUR+eQx7vVYeJAb40GZI08jPniiz8snenRjTS+LSSIhVmGQ8WQ/JjeanWmNF8/mQtHYEoQpd2c1rXPUrWOjY3hxo8OpQcwgU2h9y6m0aEF88K2JH9cNPyWwhIB8sMOyziN4xrMKC4zrBrzj33t5jW+6MOTlk30+eLLPhY0u3TSiaUOWMC5bxEMvieglEXvzhsYGwkFiNHlsSIxma1LR3EdZQqmgaHDqHgQCuKVC8H44lB4Ew3MLlYNHGeSWzqRLHBXHyXwuiciQ4GT6KRHZA6485JwW+G+9978nIj3gd0XkU8B/Dvym9/4nReSHgR8GfkhEPgJ8H/BR4DTwz0Tkg957C/xt4AeA3wZ+DfhTwK8D3w/see9fFpHvA/468OdFZBX4UeAThHX5d0XkV733e8e454fiYTvWgzKHd54sMZyKFJv9hNU8wmh4/foMrRVbC4mV2rbM5y1l7cgTw8ubXXqpZu/NmiRSTFqLaNifVbigp0+0KLk1fjnjcyduioVawAh4IU1i1jspJ3oJo3nFqouYNY5OZtjsZ7y82UMtbA1mZcvutKZoLP3UcG1cksea1fx2A27vffBResiie68m/J2khPdjJuZx9GKWigZLHAdHzny89/+h937kvf+rwP8E/F3gzz7knKve+99bfD8BXgXOAN8D/OziZT976H2+B/gF733lvf8q8DrwzSJyCuh77z/tvffAz91xzsF7/SPgOxdZ0XcBn/Le7y4CzqcIAeuR4TBz6H471oMyh/XQSTS19Qyz4P3Stp613NBJDGmsg+fPek6WGqZlQyc2WO94a2vGzqxhf1YxmgT227SwaPG0KKoWqiW54CbuXFIt0FjAebJYWOtEvLKeE2koWsdXt+eIwHje0k8M07IFH2zMz63kgAfvmZQtX7w85vOXxlyblAxzQ9lYRvOKt3fmOOe5Oi4fSl0+THuG2/swznvmZUPrAknlQXiSsoyjfBaWWOIwHpr5iEjfez9eZBIH+PzisQvsHuUHLcph/xbwGeCE9/4qhAAlIpuLl50hZDYHuLQ41iy+v/P4wTkXF+/Visg+sHb4+D3OOXxdP0DIqDh//vxRbuUmjsocOrzbfWUjyO/vz2vmTcvbeyVX9ubU1nN6JaOfRuzNGm7MamKj2Js1vLMzZV63tE7opEEVYdo2KBQ4h10Gnttw53KnCEw3EUgjxbhseHNnxva4RmlhkBlWOjGXdgu+fG3MBzZ7nOjHnBiEGStRglbC7rwhNorICOKDJ9Narrm0X3BikHB6mGOUHJu6fLBBeXt7xqXRnN1ZzWoe4x1cWO/cM3t40rKMpaLBEsfFUcpuPw/8aeB3CZ/rw39JHnjpYW8gIl3gl4H/ZhHI7vvSexy782cePv5uz7l1wPufBn4a4BOf+MSxtmnHYQ4dLnM459mZVuxOG070E/JI8+b2hNGsIjED0sjywkoHrcBITTdSuDxmZ1KwM20xYplXlkzZm5bYS9wfhiBHlEeBJr3eS9FeqHHMpg1KUorGsTlIibVwsp9wbtjh3FoHARKt6ffCR8ULXNubc3VSIl7x4dM9vEAvi1CL3/u7WXTjBREh1sKFtRwjilFRE40UL6x1bgtkT6Ju2pJFt8Rx8dCym/f+Ty8eX/Tev7R4PPg6SuCJCIHnH3jv//Hi8PVFKY3F49bi+CXg3KHTzxL6SpcW3995/LZzRMQAA0I2dr/3emQ42LFWrWM0rZiUDSuLXkBdW/ZmFbOioW3DjEjV2Jt02to51noJ47Klsg4jYYHc2p8xqxpEPFpBlkSs9VMmRcOkbBiXFUVraS3szmBWLiV1HgYPxBqMURitWMkNvY4hM2Hh1lpRu/D7MgutNW3CRyPSKuiyOc+8bvny1QkOmBQt3nv2Z01wAl2Ul46y6N6rXGa9Z95Y9uYtu7OG7Vl909/pTofRO51IlQSrjfuRHd4PLBUNljgujlJ2+8YHPX/Q07nPuULoDb3qvf+pQ0/9KvBJ4CcXj79y6PjPi8hPEQgHrwC/4723CzmfbyGU7f4C8DfveK9PA38O+Ofeey8ivwH8xCEm3Z8EfuRh9/tuUDWWi7szRkXL5b2SNBbevD5jXDU4H8ppgywmVoq1bsyJfsr2fsW1vYJYe5yCxjn25pZPv1nSWI/3Es5LNa9dG3N1r6BuIc8MzrUUNdSP42aeQTQEVYhhFiHAtVFFJ45JjSKJNJf2ZqRGo3Lho2eHnF3r3swuzq/mXFjvYLQwmtf0Es3JQU6QeQ3OpCd76ZGFO+9XLjvw/FESsqDWBUXqU4P0rkB2OMtonefqqKCxIRjda9bo/cJS0WCJ4+AoZbf/ffGYEphjf0Co9HycEAi+7QHnfivwnwGfF5HPLY79D4Sg80si8v3AO8D3AnjvvygivwR8icCU+ysLphvAXwb+HoFF/OuLLwjB7e+LyOuEjOf7Fu+1KyI/Bvybxev+mvf+SP2po8ItPvijeU3rgiTLtKz5zJv7xEZxfq3Dtf2Sz311j6+/MCSPI25MSz779i7dSFM7x+tbM3ZmNWcGMbFYJrWj8SDO8ZWrY1LjSSJNtBAmq5sWo5aeFsdBR8Egjziz2qG1cG1SsZ5b9ouWk/0MpaAXG6Z1yyCPwIedfF21NNaRRprTg4yibhl2YrzzTCrL1qTE4jmzkt8upSThb+M40jZeYL0boxTszCrwQQVhs393IDvIMq6OCi7tFcRGOL/27vpN7wYPGoZdKhoscVQ8NPh47/8EgIj8AvAD3vvPL/79MeC/e8i5/x/3b0l8533O+XHgx+9x/LPAx+5xvGQRvO7x3M8AP/Oga3wvsN7fLHUoFeZxrk8Ktqc1gyyitp5ISzCBm1akQ8POpOLL18bERjPMIi6sdphUDZWHcd0CnkQprDhG85r9Ak71FYghEsvUeqpqGXzuh4N5p4NClSaQDcoWWmdZ6yVEEbSNYzU3zFtPHmmi2LCWGK6N5mx0E2Z1KG1GWrHSidmd1ezNW6yz7MwaIqVIYs3ZYcbWpArBZxFc7kcCeFBTXouQxYbzqeH8ak5rHZ6gSH0vpJG+acXwXvtNx8GTRnRY4unFcYZMP3QQeAC8918AvuHRX9LTAy2BMgtgrePaqCQ2mjwJ8vU3xgXXxgWTomU0r7kxrRjNG6yDunXMm5atWYF1HoPQiSOKOvj3bI0rKhu02vbmddhVi9C0MP8a3/eTigNTvcPoRqC1QXnH1f05b23PKRpHkkSsdDL6mSHXCkVwGX17p+Rf/NENro5KTvQTjBK+cHmfSC+yi0XGstGP+ejpAb0sOIU2iyzmXlTjgx5PMKcL5TK4vT90k5LvoLEekVBCe1AGE2lFbPSx+k3vBUs69RKPEscJPq+KyN8Rke8QkW8Xkf+TMLfz3OJABn+1mxBrYVI15HHEx88O0OL58rUpk9LxsXN90tjwzs4U5x3nVrMwOV80tG2Q4o+M4uQwJTaaedNiPazkhmEmzGpPU1u0cuTxkt12P8SykBgi1Gb7EWgFzgfZh8ncsjNtmNWWQWr4Y+eGREp47caUN7enWOv5wGaX2MCpfsKkavESlBGUBCWKF9c7bPZTTvZT8tjcXPCB20gARiuc98zq9qZ9waVRwTCP7tuUT6OQSZ0cpJw9Qu/m/W7y30l0OLjHOwkRSyxxFBxH4eAvEvouP7j4928RVAeea6SR5pXNHudXcjb7Y/bnDTvzmldO9Di76vi6zS5OFE1j+cPLI2alZa0b0/iQ0VStY72bEBnFmWHGqW7Ep9/c5sp+xX7RUrdhOt8DZRNss5e4Nw707TZXDE1jmVaeeQWtg36uWO0mRMZgBCZ1y95CJSLRmkRrdqYlb97QKBG2ZhVrCLZ1aCW4Qwvs6WGGR5hVtwgGkVZ3UY0FuDGpiBZ+OrZ17M5qzg4yWvxNG4YDvJuS1vvZ5F/SqZd4lDiOn08pIv8H8Gve+y8/xmt66nAwsR5pRe0d1/cLJpXFOscwDwprZ4YZ33hhlTe3p4xmDZ1Y04tz+qf7zGvH3qTkyqgkjRX7lSU2QUjUW09F+EUt2W0PhiWU3sZFSyyQJ8G/RwQEhVmURFOjMCJc2p1RtpazqzmRES7vlYjM+Xdf2UC8cHVUsN6N+cipPuOyvRlsLqx1iLW6jWAA3CXYud5LuLQ35+qsDpp9eGKtKOuWNDa3BZj3MrvzfjX5l6KkSzxKHMdS4buB/42gV/miiHwDgUH23Y/r4p4WlI3l0u6c65OK6bxFRDi3kjNvWq6NSiyOzW5K1Tiq2rI9KfF4emlMbBTeezZ6CbvzGus0ShSCoqhdcCt1oXexsxRteyAsIUOsShgMDMYYdCoUTYMWKOqWVCvWBwkfPx8Y+H90ZUI3i9ifV/QyQ2YU652ELDbMqhbnPbuzms1egjHqtmylbtxdmcrhLMQ5z41Jxf68oZNomtZxaXdOPzW81Elu8/N5WhQClnTqJR4VjtPz+VHgm4ERgPf+c8ALj+Ganioc7FiTSJHHGi+LhcMGKq/Snm5smNctV/ZLemnMai+hExsuj2bsTismZUsUC+/sFlwdzSibhlnhaByULnDOR8vAcyR4goNr2bZ4b0NWagxmYcZXtpY0ifjEhTXOr3UoreXVK2Pe2p5RN5b1YcaZYYoSyGPNrLZc2pvzqVev89UbUy6NCsrFsPDDmu9eYCWL8BL8l7wIgzww0+yid3LQMzlc0oLHTx54L7hTm26JJd4NjtPzab33+w+Qxnlu0LaOeR08YaLFAqK9kEcK7zx1E2i6ZwYpxghpZLg+qSibFpUYcJ5eFlM3FodQFjVvFQ2trXhzt0SLZ+Zu6bUpltptR4UHcgXOCq2DuqyoWksnSYhjxWYv5ZWTXSrrEIRhFrM9KYmNIo0MkSg+d3HEShbTy2M0EijzSjGtWvpZxLX9klOD9K5MZVxWvLUzC2UwETZ7CZ00YqObECmFCFR1IMnrA3fTO9huy5LWEs8LjhN8viAi/ymgReQV4L8G/vXjuawnF6N5zWfe2OYDAQl4AAAgAElEQVTN7RkeeHGtQxor3rg+xQGzqgWB3XHN69cnpInhg5t98A6F5/p+wcWdObOyoXGes0OYNS1l3fDFq3PGRU3Z3B5sloHn6MiATgxWgRKP1ooo0qzmMaf6Ob004rVrM073MyZVywsbOcNOxH7RYp3j1DCjbi1XJgW9uuHcaoeiaUhjgydQpRsffiNCKOUlC5menWnNhbWcePHvrUnFyX5K04bvAV7a7JAYTdHYuwLMsqS1xPOE4wSf/wr4HwlSYj8P/AbwY4/jop5UtK3jDy+OuLJfstaNAeHS3owrewXn1jp0Ys2kbJkUNStZxMWypagsb2xNOLuaM6ktRdWwXzRMyxoQLu4X7E6LINXS1tgW6iVz9T2hsRDjKRrPSqYp26Dflighi0Jg2J3XpEYTa4P3LXXjmFY107KhtTDsxFzdLbi6V5KnhvUOdBNzs79jnac+FFTWOhFrnZjY3N6ziYzilRM9Xljv3FQ90CJ4YakQsMRzjeMEn48svszi63uA7ybI7DwXqJ2jalqUkpuLjPWe1nlOrmTgoHEwqRtGRUNiFFrAI4zLhl5i6EQaQXFxd8ZeUVNVDVoprLVUTqiWA3vvGrL4QgFa43DU1tNLwzzOjVnNrPG8cqLLiW66GCqdcWl3ymtbMzIDnTTm3FqHSCt6g4ztacVHz/SZVo6ydmGWpp+yNanopoZ+HlE3lsb5m6W0O2nISgnewvVJdRs5IYqO03JdYolnC8cJPv+AIKfzBZ7TSlCsFElkcK6kboNJsxYJmlp7BVmsubI7Z39WE6nQcxDlGcaaadFgrWOtm7LWTbg8mjPIImaVRVvLqGiZFp72a32TTykESAUSExSsO4kJUjsKlNJ479FKsdqJ6GaGcdWSecVXtiZ0kogPbOQ4D29tz1nrJJwYpCSRYq0Xc2GtQ55EzKqWMwvVgcP9njQ22KplvZfcU2D0SbRAWGKJrzWOE3xueO//6WO7kqcAxig+fm5IUbe39XxePtHjjetT9osGay3n1ztc2ZmzO6vQShimEXli2OwlVLXDiScyho4BrSyihWa/QIR7OA4tcRR4oJNCEhn0otdT1Q1ihe5As9FNeXGjQ1l7RrMat+r40uUpb2xNObuSMSpaTvViTg1SYiOMy5Y+EatZQp4GsdHY6JtySvcatuzEhs6quatn87TQqJdY4v3EcYLPj4rI3wF+k0MWMoc8ep4LDPOY7/zwSf6dQ2y3q+OSlzd67M5KXtuasTer+NjZIW9vT9ibN1wbFZhIc3FvzulBzkon4sJazqyo6KeGy3uOU4MMoWRcOjwwWyqHHhkaWIlhvZPS+tCLadtgZ7HejRAUL250eWG9y2vXx2yNK3YmNVuTkkgHt1ithNduzPngiR7DzKC04uww5dQwp2rcXeSABzHT7gwoS2WAJZa4G8eV1/kQEHGr7OaB5yr4QMiA+iYoF7iF7hcCtQvByGjNSh7zFRt2wHuF5aQxeCVc2y+4Pi44s5pTO9idlJSNo7LQSw3bs3pZejsGYsAoiGOhk4Zy2kY/YXfcsNo1DLKYM2sdtqcVndQgoulnMZEJhnCDzDCvLd4LxiheXO+y1o+oGs+F9Q5nBzlKy11SOMdhpi1p1EsscTeOE3y+3nv/xx7blTylOFhYLu3OmVeWtU5M6xxb04osMlRtiNO1h7ZqGJUtkYILazkn+inTsmSnaYk0NE4RqUBaWFbgHo6IIJ2zkivOr/XopWHmKo0Mg1zY6MVEWtGNFVkck0eGlfWEG+MSUGRR0N7p9gNpYOgNF3fnWJdxZjXnxn7Flb2SC6s5Wqu7tNbux0y7l9/Nkka9xBK34zjB57dF5CPe+y89tqt5SpFGmhfWOiCQaMWJfsrvXdyjyCJmRUMWKa6N52x0EsQ7DJpr+yHj+cMrM8rKEhnQ3rPWN/hRSw3LDOgBiAjabZkGozXjqmK/gFdO9jFas6NK9uY1JwcZdevZ6Anr3YROYvjI6T7bk4oPnx3w6uUxs9LSyyM+dLLHpd05WWroJoYr+wWzquGlzQ7REY3aHiQOuqRRL7HELRwn+Hwb8EkR+Sqh5yOA994/N1Tr+8E5jxc42Uu5sl9QW8tqHhHrBdVaeS69M6dpGhSKvGt44+qIvcKSR5pUw2haMnXQSxVpDPOliug90dGB0eaBQWYATTcVkjiiqRpmZcuwGyyuq9YFVYHWopTiRC8hMppYa071M1bymFc2unzl+hRjhKZ1rHRjNDCvGm6MKyprubpXcHolvymFc78AsmS13Y0HuZ4u8XzjOMHnTz22q3iKUTaWq6OCadWyNQ7ZzO68Yl41XNorKBrLO9tTqqplVllW85RJ2XJ93lDXDm0ds7KhqENpbl6655PHfgQIYYRnJVOUrcco2C8qysbw0maExBHzxpLWmmnVIjiujko+frZP3RAGTSPNmWEGwOVRQRZpitajWMxx1Za9omFrUmGd50QvJYk01/YLNnrpA0kCS1bb7Vi6ni7xIBzHUuHtx3khTyOc87y9E8RBr08qdqYVzjlio5lWFu8ce9OayAir3Yi3dhqcr+hGltODhK9cmyKtxRN+ESVBmXlZbrs3BGgtbE0dsRFQHguId8wriCNhUjR4FzKes8MuTnn2i5ZOXDEuG1Kjb6oMKAnzOif6KVdHBdbCyUHGRi/h+qRivZsiClrnF6W75IG79yWr7RaWWeCTiScpEz1O5rPEHWisY2tckcWKaKF6cH2/YaNn0EpQKCZljbWeaRPUrU2kaZxjZ9oSGcFaj21BDKgmBJ4l0eBuxASn0jwHI5phJyY2wt7MUjYNk6JiTSds9BK6WURiNG9sTakddOKGk8OcSdnczF4OM9AOAtD6Qm3cOU8azUgihVGKqrV4D534wR+XJavtFpZZ4JOHJy0TXQafRwCFoHXw5QFH2TRsjUsuj+bUi4UrNzBxMJ1XaG3QdYVSQieBLFKUrWXaLAPPYUSEGZ7EQCdTbPYS4iimFytmTcv+PITqfhJRtZZJHWjWndhQNsEvqRsrJlXL9rjgtS3NN55bfSgDTSnh7GrOtf2SxgYB0FPD+weRw7vJB7HanqRd5+PGMgt8svAkZqLL4PMeEOmwII6Kmm5imESK9V7KzqRiXltiY+imMC5rJkWLVtDTEd0s4tr+HFpPDdTWUrWhn7GcLQ1IWEjmxPDCeo53Qi8zFLUPPjutxXqPx2GdIo01idH005i1XsK4aGhaRes8p1ZyXlzr8NKJLqOyYbX74PIZHJ0afb/d5J27+ydt1/m4scwCnyw8iZnoMvi8ByglXFjvEI3Czu7UIKWXGn7/7T3Or+dc3i8Q5ymaltms4dXrU/IICgtrWcK0bilrCy4MSioPxTL1wRACsRI4t5JxepjTzWJ2phUruaGTRFgPe7OWlTymDf+FREqwHrQoNvspHzo5oKgaotiwmsUMsxjx3PzAHQ4IABuLstvhDOhBH8yj7iafxF3n+4HlbNOTgycxE10Gn/eIgxmfgw9YYx1ZbJhWDbb1jOY1l/YKzg9Tzq51KOqGy1tTyqahcJ4kCo1zZaF5jpkGMaAFqkXwFQUnVgwvbHY52UsoLLyynvPx82skseILF/fwePYry2zeUFrHMIsZppqVrmEjT3EK9ouGrlGsdmLWOjFaqZsW1wcBoXVwbb/g8l7B2ZWMU8PsSFnJUXeTT+Ku8/3CcrbpycCTmIkug88jwOEPWIRivRvz5vaE6+OSG/sFFpjWCymXqkI8CIpUOaz3VC04C+1zmPVEQKQgNqHk6OqFakFHMcxSRrOGnXHFWi9G5Rnb85KPr6ww7KbAhEwryCNU2bJXtqxWlmTWYNB89Gyf8ys5WoQsMmilbn7gGutCCUwptsclidGIODyeS7tzXljr3Cancy8cdTf5JO46l3j+8KRlosvg8y7gnKexYRrnwMv+4FjZWGZVw9vbU8qqxRhFZjRv787Z6KXkkeb0IOPKuKCxir15jfMh8DRf4/v6WqAXBKOZ1eFRC6QGYq1Q3nN9UiF4nBZO9HK+dHlM20CaKD56ZkDReC7tTKkSjffCuWFO5RxZomgdnOwnNNZzZpjd/F3BrYBQNxbrPFqHgLQzrZnXFgTOruQPzICOupt8Ened7xeeBZLFs3APB3iSMtFl8Dkmysby9vbspoPlejdmvZewPanYmlRsjUvGRc24dMxby96kRmu4MS7ZL2pwoPCMi2Au521w3nzeKm5CyHpaF+5dA6IhEchSTdmGkpoSQRDKwnJlUpJEmqujGR89v8Jo3tJLPOfXulRNS+uFJFZkooi1oWlDhgH+5qLRWHcb1frKqKCqLKVuUV4hCy+gRKsj9WWOupt80nad7weeBZLFs3APTyqWwecYcM5zZVQwKmp6qaFuLa9eHdNedJhIcaqXEGnhqzszvHNkRrODY3sSfH4qhKZtmVYOrWBcNlSLwKN4vhz6YsAYqG0gFmwOFFlsmFSWzID1mlODhFntGFc1KE3ZWOracmNcIKJIE41znvVewrRSrGYxG72Ua5OKG5OS9X580/SvaR2XF06icItccLKfUtQt18cl29PQ8zmzkhNHmll1tL7MUXeTT9Ku83HjWSBZPAv38CRjGXyOAes9rXXIYue6X7YYJbSKEExqixNP03rSSGOdY15byqYlFsHiaa3HtZAmYH0IOvB8BZ5BFPo8kVbszB1KoLZCNbcUrSVWEaeGCa0XVjsR07LFWsf+pCSJg11F1bZcm5TkRvGxM0MUnmntmDeWLFY45xnPW9q2ZL0bc3F3Tjc1t5ELTg9SWufpZxHd1KDVjNaF6ykXfk3Lvsy7w7NAsngW7uFJxjL4HANaBLMYJq0bS904lFbEziNemFUNrfdY57gyLoi1YqObMKstrbP41lHbYJU9C6r+lF/rm3qfcb6niGMTspjWksaBYi5KKMoWJXBqJeUbLqzz1o3gDnthJWWlmzIuW66NC/rApHLghJVeSi/V7BUtG/2YM4Mc7zxXRiVn1rKFrYXl4m5BNzNsT6vbyQV7Bd1MU1SW7VnFtLBcHZWsd1POrmbU1pGqZZnluHgWSBbPwj08yXgwnec9QkR+RkS2ROQLh46tisinROS1xePKoed+REReF5Evi8h3HTr+x0Xk84vn/oZI+O2LSCIiv7g4/hkReeHQOZ9c/IzXROSTj+J+lBJODzMGacS4bJjXLakRBp2YWdPwR1cn2Nbx7R/c5NQwo7HQTTTd2OC9Z1paqibQiedA+TylOwus91I6ccSZYcqLJ7qcH8ZoA95aUrOY61npoUU4Ncj41lfW+Uvf/jLf8OIKgyximMV4hO1pxfasIFaKLInoxRHeCt6DV8J6P6GTRCglJCYEj7JqsS6U3ZSE41uTkiu7cy7uzpkULWVjOTtM6WeGPA7WF849hzTE94iDnlpjPbOqpbH+qSNZPAv38CTjcWc+fw/4W8DPHTr2w8Bveu9/UkR+ePHvHxKRjwDfB3wUOA38MxH5oPfeAn8b+AHgt4FfIyhs/zrw/cCe9/5lEfk+4K8Df15EVoEfBT5BIFH9roj8qvd+71HcVBxpTg8z1jsxN6Y1qdG8sNZlo5OSp4Z+bPjDixG+50kijQVGF0ua9nZG2/MWe4Yahp2YaWXZnVYYo2ksRCJ0U4NHSOMI27bkiWKz1yWNNIMs5twAdtcqRKBuHUVtSY3GescbNyas5Snf/MIqSawRD5dGxc0dq/OezX6CF6FqHM55Tg0zWheyH60NDkukQ18pSyLq1mGdD9p7yzLLu8KzQLJ4Fu7hScVjDT7e+986nI0s8D3Adyy+/1ngXwI/tDj+C977CviqiLwOfLOIvAX0vfefBhCRnwP+LCH4fA/wVxfv9Y+Av7XIir4L+JT3fndxzqcIAesfvpf7OWhAJkbRSQxF3bJXtJxdvSXRPysb3r4xY68IEjuTumZnUgFBSud52EPfz4W1k2l2pg2e0BPrZxGV1cwaSyeN6CYxeRoxqSzdNOKl9S6rWcSNaU1VWzxCHhsQi9FCooVp1VLWjrWNiDy5pU5wJ7X5wlqHeGH0t72wS2itZ7OfspJFRDqcV7WWcVExqRytc4BwakHTXuL4eBZIFs/CPTyJ+Fr0fE54768CeO+visjm4vgZQmZzgEuLY83i+zuPH5xzcfFerYjsA2uHj9/jnNsgIj9AyKo4f/78Ay/8zgZkYjRKhHnVMqks86rl1Wtj5oWlaj1l6xiXLfvTAuuej8AD97/P0dwS6zmTygdF6qJlNVdESuMRVvOYXmYYZBGfOLtClkVoBNFC1ViU6vPZt/aYlS15ZNjoxXgfTONuTCoa60gW/Zn77Vh7aUQnNljvER+udW9W00sjticVJ/sx29OWjW5CFhtWsoitScX5SC93vUss8QjxJBEO7vXJ9g84/m7Puf2g9z8N/DTAJz7xiQfGh5uDiW2Q/2ytY7UTcX1SEamQDb2wlvGpL24h3pNoRVOXXNx/3gps98bMwcV9jwbSxJNqy7X9kOVMi4YbpiKOFcNOwtVJxfjGjLqxdDPD+ZUOr5zo4zx89caEL1ydEBnDIDNoBduz+q7ezP12rIePH2REzUKbb6UTsz2tbmq8KZEjU66XWGKJo+NrEXyui8ipRdZzCthaHL8EnDv0urPAlcXxs/c4fvicSyJigAGwuzj+HXec8y/f64UrJQzziN9/e4/r4xLnHa+c6DLIDbnRTOuGr1yb8JVrY+aNxdqWnbGj5jEzO54SxIvHLA6mcE45nIco9hgMDZZEa9Y6CVuTktGsYVI1JFqxM6742NkBmVasdGNO9xM6qV4QCISN/G6l6qNMpqeR5sIhbT6AcRFo1gdMpyXDaYklHj2+FsHnV4FPAj+5ePyVQ8d/XkR+ikA4eAX4He+9FZGJiHwL8BngLwB/8473+jTw54B/7r33IvIbwE8cYtL9SeBH3uuFO+fZm9U0LjS8LZ5//foOVePIU81Xt6bcmJTszupAr26gOjj3vf7wpxwCJBEUDVQLOQfrwqyTOM16P2FzkPCBEx20gq1Zw6RssM5TNQ1v3ZjxlRsTVvOEfmZY76c46xjkCSf7KScHt/dljjOZfmeG9LxK4SyxxPuJxxp8ROQfEjKQdRG5RGCg/STwSyLy/cA7wPcCeO+/KCK/BHyJMPT/VxZMN4C/TGDOZQSiwa8vjv9d4O8vyAm7BLYc3vtdEfkx4N8sXvfXDsgH7wXWe4q65e2dGd3YoI3wzvaUsrGcS3Iu7hbszguU9hh4bggGR4VzkKgQdJIIlII0MhijEFEY0VwZlexOGyIdvHg0wsX9kkQLgzTiA+sdau95eb1LHCu0F6IF+/AgQLzXyfQlw2mJJR4/Hjfb7T+5z1PfeZ/X/zjw4/c4/lngY/c4XrIIXvd47mfg/2/vzmMsy6vDjn/P3e99e+1VXb1MzwzTjAeGWcAmGCs2GJHEgkhJFMeOg0IUBErAjoIdHKRsCIfEVhJLieygMRkkT0xsQhwUJQZkBxNZbGYMMwyYMTTj3pfqWt9+l5M/7q2a19VV3fW6q+tVVf8+Uqlfvb7v9nlV/d55v9/9/c7hYzsOdgdsEXpJysJan6SUkaSQZIrvWHSTjE4/odlJiDMlzfLqzI7ee3XbtiLFl+eBawtT1YjJskfgOiSqBI6FbQnjkU/oWaz1U9rdBMe1sBGqYX5sFLhkvQTXtbh/ooJlyQ0JYjd2ppsVToZxd+2nBQcHgu/Y1EI3bwKH5q2zBU5fWiVO8hYJaaokGWhRPsfGdCh1gfGyReQ6YFnM1wPGSgH1kse1Zpdy4DJXj3h4rsrltR5zjsV45LHa7tOLUyLPoRK6JMWywcC2r6tSPcjsTDeM/c8knyGkqgSew5senuabF5bJMqXkOVxabnOlGRP5FioBzW4f10rpp+DagMJqL5+GO+xcwBNoDcw3ekAtAs9xKUUejlgkKXkdtzhlshIy1wi5b7xE4DqMhYoKvGo+ZKHZ51VHG/z5YhvNYK2XcGqmwpGbTKHdTguDw1Q23zAOApN8hrD+iTpybZ48OkYnTriw3OXERIRtWSys9bi02sG2lCSxaccZji10ewlaXP1xOZx9e8o2hA6USg6dXkqQKK0e1KP8uo7rWni2zSsmyoShh2sJxycjpioBtdAjdG0WWzFxN2GqGnB0LMJ1rHwvjsBrjyv9LG+H4O9gz80w121M2XzD2Hsm+Qxhfan1N8+vkGb5JsV6yaWb2Nw3VcayhAtrHabKEUmasNDuk6RQjSxWOz1iPRzTb+sVDJziTxuIXKhGPtXAZnIqpJ+kLLZjyn6+HLrVz/BsC9exmaz4TJTz9gcnx8vEmjd7W7fldJoNHjdPCJtHLzu5bmPK5hvGaJjkM4QsU5bbMUcbIQqoKi9earLc6bHS7hP3U47UAubHSiy3e8iVNi8tNfNmZcU01GFacp0BkQOTZZeZWkg1dMgyCDybH7x/kpJv8a2La5xfbjNddTg+USL0bfr9hMsrGeMln/MrHRolD1sEFW572mvz6GWq4uM61i3Ptx/K5pspP+NeZJLPEFJVOnFCq5eSFW9al1Y7RJ5NFHjUqxnxKogIpxc6nF9ustRMDt2aqfXRjmtBxRNmaiGZZIjYvOHUGL5l47sOjx6psdpJ6PUzJso+q72Udj+h00+ZrAR872qLExMRkW9zZqkNcFvTXptHL81uzLNnlpipBTiWddPzjXpxgpnyM+5VZuP9EEThWrMPqkSeQ5KkNHsJke8wXfGZqkZMVH2+e3mN6bJHhoVjQyc5XCOedY4FvUy5vNpjLPQ5OVlC1eLkZIUkU5Y6fa6u9Ziu+lQjF0eU71xaI3JtHpqrMlcPiwrXfWzJ21e7tgzdxuDl0UveRG6pHWOLELr2Lc83yrL5g0nzdp+7YRxUJvkMQQXGSx5iCe1+guva1Ip22q5j0UsyXGHjzcR3bCLPOpQbTT0LKJrA2bZFO065tNqjn6TEWcpMLeD4eImj4yXqZY8rqz3iNE/akzWflXZM6Nn0kpQkzfDcl6e9Ms2noXZqcPSSqtJPUjw3v260k/OtL044OhZxbCzas5HHYNKE23vuhnFQmWm3IdgihJ5D5Nsb5Ut92+J7V1pcXulyZbVLxbdYbMUsNHt0+yntfnag9/lYvLzAILTBsyFTqHhguy6lwKHkuNw3UWKhlXBpucOpqSqPHq9T9hyO1COW2z2yOqAZgWszXYtYacestHrYYjFXD8kyxbJvr5ba4NLqNM3rxTVCd6jabKPYVDrqKT/DGCWTfIawvtrt+XMr9NMUz7Z51XyNkxNl/uj0AolmnF/sMl1xOX2tTegoCzHYFgdq3s0GKi54jvDAZES7r6gogeviezb1yGO52cdzLVxbmK2GVEsevpfxxgfGeeL4OKGf/9d6/HiDr59dopfklSAePTbGucUOnmNRK/k8erRO5Dl3XEttcGn1bD3kylqPVm9/12a7nf1IhnFYmOQzhCxTLq92cSxwir4xl1e7HGtEOGJR9R1WA4tmz2OuppzPlGqQ0o4zOgck+VhAyYP5sYCHZ2qcOlpHM2Ws5BP6NheXu5Rci2ac4opFq9NnshbhWEI1dDjSKOG79sYKrmrg8sP3T7Lai1lq9hFLaEQejZJH1XdxnHzKaTdqqa2PXlzb4phrH4gVZKaOnHGvMslnCHGacWW1RyVwNqZJrqz2mK74XF3rEmfKQrPP5dUOq90YbMH3LdpJtm13z1Gwiy8h3/C6nhdLgOvBkbGQ+VrI/bNVnphvkAlUPZdvXV5juhyw2ImZLod0k4y5esTVZo/xss9sPWS2HtIv9soMruAaK/nUQ2/bN9ndnvY6SLXZDlKshrFbTPLZDQqKECcpcaasdRKWWjGWKL7rELlKr59utFcYlcr69ZoQunFe+DTJIE3zCtNjkcOJiTIPzlaZLHnM1yNs22K5FdPqpXTjhPFSQNrqE3oOcRZz32SJI42Q+UaUt7gGziy2t920ebfeZM1eGcM4WEzyGYJrW0xVfJY7ffpphqoyVfHxXZupqsfZxYRq4FIJHVzb4vJqm7VOzGo3G2ldNwso2eD5ELo205UQC1jrpTiWErg2oWczFvmoZdGLM1IFSywqoUsvVZIkZa2TEDoxtiV0+gl2UVE6cB2iovNnnGZ7vmnT7JUxjIPHJJ8hWJZwfKKEu2wRxyliC/P1KE9KZZ+rq13Kno0DdOIEVQvPdhCrv6fL3WwgsKDkgOsLcQqVwKEe+kxWfSqBS8l3uLrWoZdAq59SDV0mahH1wGWi5jNV9gHBc2wmyj4LzR4z1YBWP6UROnSTjPunSijXXyTf6xVcpjyOYRxMJvkMKXBtZqoBF5Y7IHB2sQ0CtmVxrdUHhKlqvtqqVrKJYwgdh8trCWt3OQcJUPXh5HiIWkIjDJipeFxY69OP+wSez7HxMpXQIU4ymr2EuZrHeNXnxESJpbWY150cw3VsbBFOL7ToxymB5zBR9ii5NsfqETiCgyC2bBT+zDLdqKe2lyu49kN5HMMwhmeSz5CyTLmy1iP08srKZ661UIXZasBkOaDVj5mp+5xddFnpxfiOTT9TapHiSMrCXbrwU3OhEgiPzI/x4HSVlU5MreQTOEIU9Ek149RMjW6S8tJCC0uEcuBRCj0myyEztYjVzhpppviWkGXKVNUnVVhs9rjW6jNe9rjS7jNTC/Bce9vprr1cwWX2yhjGwWSSz5AGP2knaYaIIJLfH/kOgWvRTVxmGhGrl9dIVQnFQj2PsQi81Q5LLejqcKvfHCC08sdkCo4N/TRvWNeI8vpljZLPI0cbHB8rsdyOOT4WEQUOp6+0WGp38Vwb27aohT6vOVbl7GIXR/JqDe1uTC10ubTaQ1d72JbwyJEaZc/hpcUWx8ciPPflaa35enjT6a69WsFl9soYxsFkks+QBj9pW5agqqjmHU7rocNCq8dau0/FszlaD/Edh8VWh9AXVlop1dDj8lqHhdU+rR5YkjecswXKkUWSCGudlG6aT1f/5GIAAA3zSURBVNFFFlQiIcmUmYqPWA5l36EWufz5YpOVZsLsRMRcLcJGiRyLY40Q37PzkUCmhJ7NRLXKfeMl+kmKKtRDn2Da4cVLTZq9mG6SMVsLaJQ8LBEyzSt4h66NJXJd+ZtektDPbr6wYC9Xn5m9MoZx8JjkM6TBT9pZklEPPRDopRnTtZBGySNwLDLypcyqQqNcwxIYK8UIFpHvMFVN0VRxHAvbsmj2Eo41QlIVziw0We7F9OKUsZJPq5ciKEfGS4yVAmwRWv2EkxNVurWUWpjXm3MsCFyXWsmnmypZpszVQiYij0urvbz2nG3zytkKrV6KbVk8MFWiHDg8MFHhcrOH57y8SqzVS/LnvMW0lmdZ2053jWL1mdkrYxgHi0k+t2HzJ21g43aWKaHncHKizNfPLXP6SotunHJ0LOJkVCZNMx6Zr5Kp4DmC71iMl3xmawELaz2uNHskacbzZ1e51mxzrRXz0JTHQjumUQ6phy6zdZ+ra30mKx5nrnUpBTb9vhIFFp6Xd/+cqYWcudamG6coMF7ORzRK/ner7ZhUFdfON4d6toXV6t+QTFzb2nJay3G2vh8wq88Mw7glk3xu0+ZP2oO3p6t5IvmBuRpZqqQKExWfOMlQK69yPRm6ZMCj83VqkYdlCZOVkOhai8C1KQcuF5Yjvvb9RebGIuYnBM+26CYZU9WQh2fruI7Q6l9jqRmTphleKqSpcrnZY7zkcaQRMlsNuLjaxffzKbhLKx0uLneZqwfM1kJKxf4cYNtrJ4G19bTWVtNdo9jnYxjGwWOSzy5q95KNJdiWCCcmyhxvlHj27BJpltGN82ko37Wpl32a3Zhr7T61yAPyTayeYyNA5LkcHxNWOzHTlQDfc6j5Nu0k4w33TZAJPHtmiQenKiyVY9pxjG/ZzDUi0lS5uNLl8WONjdppAOeX27giuI5gW8K1Zp/S2Mv/BW527WS7aa3N95vVZ4Zh7IRJPruk3Ut49swStgiea9EIXa41+0xVfKZrAReXO3TiFEeE+bGILNM80Sgbo4LB60kl3+ZanPLo0TrnlrqULEgRnjg+RhA4xGnGbDXA9/KprQvLHSxLOFIPsUToxOlGG+k4zXhpocn3rrZwLaEaucxUAjL0hhHJnV47MavPDMPYCZN8dkGWKRdWOlgCldAlSTOWOjGVwOHCSodq4FKf9ZitBbxwYY1OPyFwHRqRi21Z140KBkcfD07mGzgfm1cSFM+yNkYytuRN3IR8pZ0lgpC/+SfJ9aONLFNWOjGOLTiWhS1wcaXDTC28KyMSs/rMMIxbMcnnNg0uJU5VEQXPsTemmzqdmMxVbFs2OlWOlQNeOQu2JbhWvsrtZqOC9f0y2OBt8ffjZY+FtR4KNEoevTjl9JUWAFNVn36a5fXXLGG2HjJXD1lux3TilF6SMVHx71pi2DyCMoU/DcMYZJLPbdi8lHiq4mPbFo3IZakd0+73yRTmGxELm1aQlXyX+XqIClu+Ee9kmfLgMQCTFZ/QsTm71KYauviOTaa6sRnUGRghTVYs2v1kY7HBKH5epvCnYRjWrQ8xBg0Wsiz5Dq4tXFnr5QnIyq/1TFUDHj/WoBy6zNQC4lRp9RLiVDeWKbu2dUPi2ercl1a6ZJlue4zvWFxr9klV81bXxeo1x7bIVFGBuXpIPfRY6ya0eimTlYAjjb1Z+ryT52QYxr3HjHyGtF0hS9exdrwcedhzDy4K2O4Y2HozqC2C61o8OF3hxEQJYMvEd7eYwp+GYWzFjHyGNLiUGLjuTd6yZMs39u3uH+bctzpmfTPo5lHW+r9pWfkSb9+19/Say06ek2EY9x4z8hnS3VxKvJNz3+yY7TaDjpJZem0YxlYOffIRkbcCv0reY+0pVf3InZ7zbi4l3sm5b2cz6CiZpdeGYWx2qJOPiNjAfwJ+HDgHfFVEPq2q37rTc9/NN/mdnHs/JpmbOWjxGoZxdx32az6vA76rqqdVtQ98Anj7iGMyDMO45x3qkQ9wBDg78P054AcHDxCRdwHvKr7ticg39yi2OzEBLIw6iB0wce4uE+fuOQgxwsGJ86FhH3DYk89W8zzXbTBR1Y8CHwUQkT9W1Sf3IrA7YeLcXSbO3XUQ4jwIMcLBinPYxxz2abdzwNGB7+eBCyOKxTAMwygc9uTzVeBBEblPRDzgJ4FPjzgmwzCMe96hnnZT1URE/iHwGfKl1h9T1Rdu8pCP7k1kd8zEubtMnLvrIMR5EGKEQxynqJoaW4ZhGMbeOuzTboZhGMY+ZJKPYRiGsedM8imIyFtF5Dsi8l0R+cCo49mKiBwVkf8rIt8WkRdE5GdHHdN2RMQWkT8Rkf816li2IyJ1EfmkiPxp8TN9/ahj2oqI/KPi9/1NEfktEQlGHROAiHxMRK4M7o0TkTER+ZyI/FnxZ2OUMRYxbRXnLxe/9+dE5H+ISH2UMRYx3RDnwN+9X0RURCZGEdumWLaMU0TeW7yHviAi//ZW5zHJh+vK8Pwl4GHgb4nIw6ONaksJ8I9V9ZXADwH/YJ/GCfCzwLdHHcQt/Crwe6p6CniUfRiviBwB3gc8qaqPkC+c+cnRRrXhaeCtm+77APD7qvog8PvF96P2NDfG+TngEVV9NfAi8It7HdQWnubGOBGRo+Qlws7sdUDbeJpNcYrIj5JXj3m1qv4A8Cu3OolJPrkDUYZHVS+q6rPF7TXyN8sjo43qRiIyD/wV4KlRx7IdEakCPwL8BoCq9lV1ebRRbcsBQhFxgIh9sldNVb8ALG66++3Ax4vbHwf+6p4GtYWt4lTVz6pqUnz7JfI9gCO1zc8T4N8Dv8CmDfKjsk2c7wE+oqq94pgrtzqPST65rcrw7Ls39UEicgJ4DPjyaCPZ0n8gf7Fkow7kJk4CV4H/UkwPPiUipVEHtZmqnif/FHkGuAisqOpnRxvVTU2r6kXIPywBUyOOZyfeCfyfUQexFRF5G3BeVb8x6lhu4RXAG0XkyyLyhyLy2ls9wCSf3C3L8OwnIlIG/jvwc6q6Oup4BonITwBXVPVro47lFhzgceDXVPUxoMX+mCK6TnHN5O3AfcAcUBKRvz3aqA4PEfkg+XT2M6OOZTMRiYAPAv9s1LHsgAM0yC8H/Dzw2yI37xhpkk/uwJThERGXPPE8o6qfGnU8W3gD8DYReYl8+vLHROQ3RxvSls4B51R1feT4SfJktN+8Gfi+ql5V1Rj4FPAXRhzTzVwWkVmA4s9bTr+Mioi8A/gJ4Kd1f254vJ/8Q8c3itfTPPCsiMyMNKqtnQM+pbmvkM963HRxhEk+uQNRhqf4JPEbwLdV9d+NOp6tqOovquq8qp4g/zn+garuu0/qqnoJOCsi69V43wTccZ+nu+AM8EMiEhW//zexDxdGDPg08I7i9juA/znCWLZVNJn8J8DbVLU96ni2oqrPq+qUqp4oXk/ngMeL/7v7ze8CPwYgIq8APG5RjdskH/IyPMB6GZ5vA799izI8o/IG4GfIRxNfL77+8qiDOsDeCzwjIs8BrwF+acTx3KAYmX0SeBZ4nvw1uy9KrojIbwFfBB4SkXMi8veAjwA/LiJ/Rr5C6447B9+pbeL8j0AF+FzxOvr1kQbJtnHuO9vE+THgZLH8+hPAO241mjTldQzDMIw9Z0Y+hmEYxp4zyccwDMPYcyb5GIZhGHvOJB/DMAxjz5nkYxiGYew5k3wMwzCMPWeSj2HcJSLyeRF5srj9v3ezbL+IvFtE/s5unc8w9poz6gAM416gqru6GVhVR74p0jDuhBn5GMYAETlRNBl7qmje9oyIvFlE/qhokPY6ESkVDbW+WlTEfnvx2FBEPlE0KPtvQDhw3pfWG4GJyO+KyNeKplvvGjimKSIfFpFviMiXRGT6JnH+CxF5f3H78yLyb0TkKyLyooi8sbjfFpFfEZHni5jeW9z/piLu54vn4Q/E+Esi8kUR+WMReVxEPiMi3xORdw/82z9fPPfnRORf7uovwLhnmORjGDd6gLzR3KuBU8BPAT8MvB/4p+SVhv9AVV8L/Cjwy0U7hvcA7aJB2YeBJ7Y5/ztV9QngSeB9IjJe3F8CvqSqjwJfAP7+EDE7qvo64OeAf17c9y7ywpSPFTE9I3kX1KeBv6mqryKf/XjPwHnOqurrgf9XHPfXySsV/ysAEXkL8CB5D6zXAE+IyI8MEadhACb5GMZWvl8UdcyAF8g7cyp5bbUTwFuAD4jI14HPAwFwjLw53W8CqOpzwHPbnP99IvIN8iZmR8nfzAH6wHrb8a8V/9ZOrVc4H3zcm4FfX2+apqqLwEPF83uxOObjRdzr1gvqPg98WVXXVPUq0C2uWb2l+PoT8npzpwbiN4wdM9d8DONGvYHb2cD3GflrJgX+mqp+Z/BBRfuSmxdTFPmL5Enh9araFpHPkycvgHigGGPKcK/P9RgHHydbxHPTHitc/1w3/xyc4vH/WlX/8xCxGcYNzMjHMIb3GeC9682yROSx4v4vAD9d3PcI+bTdZjVgqUg8p8intO6WzwLvlrz9NiIyBvwpcEJEHiiO+RngD4c452eAd0re0BAROSIiB6FbqbHPmORjGMP7EOACzxUl5D9U3P9rQLlo0fALwFe2eOzvAU5xzIfIp97ulqfI+wE9V0zz/ZSqdoG/C/yOiDxPPqLZ8cq5ooX3fwW+WDz+k+StCQxjKKalgmEYhrHnzMjHMAzD2HNmwYFh7GMi8kHgb2y6+3dU9cOjiMcwdouZdjMMwzD2nJl2MwzDMPacST6GYRjGnjPJxzAMw9hzJvkYhmEYe+7/Aw3FAtDlmYzbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind = 'scatter', x = 'median_income', y = 'median_house_value', alpha = 0.1)\n",
    "plt.axis([0, 16, 0, 550000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 50万美元是一条清晰的线，被设置上限了，45 35 28万可能是异常值，后期可以考虑删除数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 在给机器学习算法输入数据之前，我们识别了一些异常数据，需要提前清理，也发现了不同属性之间的某些联系，跟目标属性的联系\n",
    "   2. 某些属性分布显示出了明显的‘重尾’分布，对进行转换处理，计算其对数\n",
    "   3. 在给机器学习算法输入数据之前，尝试各种属性组合\n",
    "   4. 每个项目不一样，思路基本相似。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 试验不同的特征组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \\\n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY   \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY   \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY   \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY   \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY   \n",
       "\n",
       "  income_cat  \n",
       "0          5  \n",
       "1          5  \n",
       "2          5  \n",
       "3          4  \n",
       "4          3  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['rooms_per_household'] =  housing['total_rooms']/housing['households']\n",
    "housing['bedrooms_per_room'] =  housing['total_bedrooms']/housing['total_rooms']\n",
    "housing['population_per_household'] =  housing['population']/housing['households']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\n",
       "median_income               0.688075\n",
       "rooms_per_household         0.151948\n",
       "total_rooms                 0.134153\n",
       "housing_median_age          0.105623\n",
       "households                  0.065843\n",
       "total_bedrooms              0.049686\n",
       "population_per_household   -0.023737\n",
       "population                 -0.024650\n",
       "longitude                  -0.045967\n",
       "latitude                   -0.144160\n",
       "bedrooms_per_room          -0.255880\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关联矩阵\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习算法的数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>286600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>340600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>254500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17606       710.0       339.0         2.7042            286600.0   \n",
       "18632       306.0       113.0         6.4214            340600.0   \n",
       "14650       936.0       462.0         2.8621            196900.0   \n",
       "3230       1460.0       353.0         1.8839             46300.0   \n",
       "3555       4459.0      1463.0         3.0347            254500.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "17606       <1H OCEAN          2  \n",
       "18632       <1H OCEAN          5  \n",
       "14650      NEAR OCEAN          2  \n",
       "3230           INLAND          2  \n",
       "3555        <1H OCEAN          3  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到一个分层抽样代表全局数据集的 训练集和测试集\n",
    "strat_train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将数据集的数据和标签分开\n",
    "housing_lables = strat_train_set['median_house_value'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
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       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
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       "      <td>&lt;1H OCEAN</td>\n",
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       "    <tr>\n",
       "      <th>18632</th>\n",
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       "      <td>&lt;1H OCEAN</td>\n",
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       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "17606       710.0       339.0         2.7042       <1H OCEAN          2  \n",
       "18632       306.0       113.0         6.4214       <1H OCEAN          5  \n",
       "14650       936.0       462.0         2.8621      NEAR OCEAN          2  \n",
       "3230       1460.0       353.0         1.8839          INLAND          2  \n",
       "3555       4459.0      1463.0         3.0347       <1H OCEAN          3  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = strat_train_set.drop('median_house_value', axis = 1)\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 10 columns):\n",
      "longitude             16512 non-null float64\n",
      "latitude              16512 non-null float64\n",
      "housing_median_age    16512 non-null float64\n",
      "total_rooms           16512 non-null float64\n",
      "total_bedrooms        16354 non-null float64\n",
      "population            16512 non-null float64\n",
      "households            16512 non-null float64\n",
      "median_income         16512 non-null float64\n",
      "ocean_proximity       16512 non-null object\n",
      "income_cat            16512 non-null category\n",
      "dtypes: category(1), float64(8), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据清洗（total_bedrooms有空值 ）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * 大多数机器学习算法无法在缺失的特征上工作，闯将一些函数辅助，total_bedrooms有缺失\n",
    "   * 放弃这个区域\n",
    "   * 放弃这个属性\n",
    "   * 将缺失值设置为某个值，（0， 平均数或许中位值）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
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       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4629     -118.30     34.07                18.0       3759.0             NaN   \n",
       "6068     -117.86     34.01                16.0       4632.0             NaN   \n",
       "17923    -121.97     37.35                30.0       1955.0             NaN   \n",
       "13656    -117.30     34.05                 6.0       2155.0             NaN   \n",
       "19252    -122.79     38.48                 7.0       6837.0             NaN   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "4629       3296.0      1462.0         2.2708       <1H OCEAN          2  \n",
       "6068       3038.0       727.0         5.1762       <1H OCEAN          4  \n",
       "17923       999.0       386.0         4.6328       <1H OCEAN          4  \n",
       "13656      1039.0       391.0         1.6675          INLAND          2  \n",
       "19252      3468.0      1405.0         3.1662       <1H OCEAN          3  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = housing[housing.isnull().any(axis = 1)]  # housing.isnull()判断缺失值  any() 取出为空的值\n",
    "rows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(add_indicator=False, copy=True, fill_value=None,\n",
       "              missing_values=nan, strategy='median', verbose=0)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 放弃这个区域  删除这些数据\n",
    "# rows.dropna(subset= ['total_bedrooms'])\n",
    "# 放弃这个属性\n",
    "#row.drop('total_bedrooms', axis = 1)\n",
    "# 创建一个imputer实例，用中位数替换该属性的缺失值。\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(strategy = 'median')    #imputer  数据估算器\n",
    "\n",
    "# 使用fit() 方法将imputer实例适配到训练集\n",
    "housing_num = housing.drop('ocean_proximity', axis = 1)\n",
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409,    3.    ])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409,    3.    ])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89  ,   37.29  ,   38.    , ...,  339.    ,    2.7042,\n",
       "           2.    ],\n",
       "       [-121.93  ,   37.05  ,   14.    , ...,  113.    ,    6.4214,\n",
       "           5.    ],\n",
       "       [-117.2   ,   32.77  ,   31.    , ...,  462.    ,    2.8621,\n",
       "           2.    ],\n",
       "       ...,\n",
       "       [-116.4   ,   34.09  ,    9.    , ...,  765.    ,    3.2723,\n",
       "           3.    ],\n",
       "       [-118.01  ,   33.82  ,   31.    , ...,  356.    ,    4.0625,\n",
       "           3.    ],\n",
       "       [-122.45  ,   37.77  ,   52.    , ...,  639.    ,    3.575 ,\n",
       "           3.    ]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = imputer.transform(housing_num)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  income_cat  \n",
       "17606       710.0       339.0         2.7042         2.0  \n",
       "18632       306.0       113.0         6.4214         5.0  \n",
       "14650       936.0       462.0         2.8621         2.0  \n",
       "3230       1460.0       353.0         1.8839         2.0  \n",
       "3555       4459.0      1463.0         3.0347         3.0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(X, columns = housing_num.columns, index = housing.index)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 9 columns):\n",
      "longitude             16512 non-null float64\n",
      "latitude              16512 non-null float64\n",
      "housing_median_age    16512 non-null float64\n",
      "total_rooms           16512 non-null float64\n",
      "total_bedrooms        16512 non-null float64\n",
      "population            16512 non-null float64\n",
      "households            16512 non-null float64\n",
      "median_income         16512 non-null float64\n",
      "income_cat            16512 non-null float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "housing_tr.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 处理文本和分类属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19480</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8879</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13685</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "17606       <1H OCEAN\n",
       "18632       <1H OCEAN\n",
       "14650      NEAR OCEAN\n",
       "3230           INLAND\n",
       "3555        <1H OCEAN\n",
       "19480          INLAND\n",
       "8879        <1H OCEAN\n",
       "13685          INLAND\n",
       "4937        <1H OCEAN\n",
       "4861        <1H OCEAN"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing[['ocean_proximity']]\n",
    "housing_cat.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     7276\n",
       "INLAND        5263\n",
       "NEAR OCEAN    2124\n",
       "NEAR BAY      1847\n",
       "ISLAND           2\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 4, ..., 1, 0, 3])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将文本转化成对应的数字分类，使用转化器\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "enconder = LabelEncoder()\n",
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat_enconder = enconder.fit_transform(housing_cat)\n",
    "housing_cat_enconder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enconder.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 机器学习算法以为两个相近的数字比远的数字更相似，比如0,4比0,1相似度更高；\n",
    "   2. 创建独热编码，当<1H，第0个属性为1，其他为0.列别是Inland时候，另一个属性是1，其他为0,1是热，0是冷，独热编码\n",
    "   3. OneHotEncoder编码器，fit_trans需要二维数组，转换housing_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 4, ..., 1, 0, 3])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_enconder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [0],\n",
       "       [4],\n",
       "       ...,\n",
       "       [1],\n",
       "       [0],\n",
       "       [3]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_enconder.reshape(-1 , 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_hot = encoder.fit_transform(housing_cat_enconder.reshape(-1 , 1))\n",
    "housing_cat_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从稀疏矩阵 转化成numpy\n",
    "housing_cat_hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17606     <1H OCEAN\n",
       "18632     <1H OCEAN\n",
       "14650    NEAR OCEAN\n",
       "3230         INLAND\n",
       "3555      <1H OCEAN\n",
       "            ...    \n",
       "6563         INLAND\n",
       "12053        INLAND\n",
       "13908        INLAND\n",
       "11159     <1H OCEAN\n",
       "15775      NEAR BAY\n",
       "Name: ocean_proximity, Length: 16512, dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1],\n",
       "       ...,\n",
       "       [0, 1, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 一次完成两个转换，文本-整数类型-独热类型\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer()\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.int32'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输出稀疏矩阵\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer(sparse_output = True)    #sparse_output = True 输出稀疏编码\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 自定义转化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix =[list(housing.columns).index(col) for col in ('total_rooms', 'total_bedrooms', 'population','households')]\n",
    "\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix\n",
    "\n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self,add_bedrooms_per_room = True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, X, y = None):\n",
    "        return self\n",
    "    def transform(self, X, y = None):\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]\n",
    "        \n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room = False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89, 37.29, 38.0, ..., 2, 4.625368731563422,\n",
       "        2.094395280235988],\n",
       "       [-121.93, 37.05, 14.0, ..., 5, 6.008849557522124,\n",
       "        2.7079646017699117],\n",
       "       [-117.2, 32.77, 31.0, ..., 2, 4.225108225108225,\n",
       "        2.0259740259740258],\n",
       "       ...,\n",
       "       [-116.4, 34.09, 9.0, ..., 3, 6.34640522875817, 2.742483660130719],\n",
       "       [-118.01, 33.82, 31.0, ..., 3, 5.50561797752809,\n",
       "        3.808988764044944],\n",
       "       [-122.45, 37.77, 52.0, ..., 3, 4.843505477308295,\n",
       "        1.9859154929577465]], dtype=object)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38</td>\n",
       "      <td>1568</td>\n",
       "      <td>351</td>\n",
       "      <td>710</td>\n",
       "      <td>339</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.62537</td>\n",
       "      <td>2.0944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14</td>\n",
       "      <td>679</td>\n",
       "      <td>108</td>\n",
       "      <td>306</td>\n",
       "      <td>113</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>6.00885</td>\n",
       "      <td>2.70796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-117.2</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31</td>\n",
       "      <td>1952</td>\n",
       "      <td>471</td>\n",
       "      <td>936</td>\n",
       "      <td>462</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.22511</td>\n",
       "      <td>2.02597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25</td>\n",
       "      <td>1847</td>\n",
       "      <td>371</td>\n",
       "      <td>1460</td>\n",
       "      <td>353</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.23229</td>\n",
       "      <td>4.13598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17</td>\n",
       "      <td>6592</td>\n",
       "      <td>1525</td>\n",
       "      <td>4459</td>\n",
       "      <td>1463</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.50581</td>\n",
       "      <td>3.04785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0      1   2     3     4     5     6       7           8  9       10  \\\n",
       "0 -121.89  37.29  38  1568   351   710   339  2.7042   <1H OCEAN  2  4.62537   \n",
       "1 -121.93  37.05  14   679   108   306   113  6.4214   <1H OCEAN  5  6.00885   \n",
       "2  -117.2  32.77  31  1952   471   936   462  2.8621  NEAR OCEAN  2  4.22511   \n",
       "3 -119.61  36.31  25  1847   371  1460   353  1.8839      INLAND  2  5.23229   \n",
       "4 -118.59  34.23  17  6592  1525  4459  1463  3.0347   <1H OCEAN  3  4.50581   \n",
       "\n",
       "        11  \n",
       "0   2.0944  \n",
       "1  2.70796  \n",
       "2  2.02597  \n",
       "3  4.13598  \n",
       "4  3.04785  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(housing_extra_attribs)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38</td>\n",
       "      <td>1568</td>\n",
       "      <td>351</td>\n",
       "      <td>710</td>\n",
       "      <td>339</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.62537</td>\n",
       "      <td>2.0944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14</td>\n",
       "      <td>679</td>\n",
       "      <td>108</td>\n",
       "      <td>306</td>\n",
       "      <td>113</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>6.00885</td>\n",
       "      <td>2.70796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.2</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31</td>\n",
       "      <td>1952</td>\n",
       "      <td>471</td>\n",
       "      <td>936</td>\n",
       "      <td>462</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.22511</td>\n",
       "      <td>2.02597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25</td>\n",
       "      <td>1847</td>\n",
       "      <td>371</td>\n",
       "      <td>1460</td>\n",
       "      <td>353</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.23229</td>\n",
       "      <td>4.13598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17</td>\n",
       "      <td>6592</td>\n",
       "      <td>1525</td>\n",
       "      <td>4459</td>\n",
       "      <td>1463</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.50581</td>\n",
       "      <td>3.04785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "17606   -121.89    37.29                 38        1568            351   \n",
       "18632   -121.93    37.05                 14         679            108   \n",
       "14650    -117.2    32.77                 31        1952            471   \n",
       "3230    -119.61    36.31                 25        1847            371   \n",
       "3555    -118.59    34.23                 17        6592           1525   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "17606        710        339        2.7042       <1H OCEAN          2   \n",
       "18632        306        113        6.4214       <1H OCEAN          5   \n",
       "14650        936        462        2.8621      NEAR OCEAN          2   \n",
       "3230        1460        353        1.8839          INLAND          2   \n",
       "3555        4459       1463        3.0347       <1H OCEAN          3   \n",
       "\n",
       "      rooms_per_household population_per_household  \n",
       "17606             4.62537                   2.0944  \n",
       "18632             6.00885                  2.70796  \n",
       "14650             4.22511                  2.02597  \n",
       "3230              5.23229                  4.13598  \n",
       "3555              4.50581                  3.04785  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs = pd.DataFrame(\n",
    "    housing_extra_attribs, \n",
    "    columns = list(housing.columns) + ['rooms_per_household','population_per_household'],\n",
    "    index = housing.index\n",
    ")\n",
    "housing_extra_attribs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征缩放"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 最重要也是最需要应用在数据上的转换器，就是特征缩放，输入数值属性有很大的比例差异，会导致机器学习算法性能表现不佳\n",
    "   2. 房间总数 范围6-39320 收入中位数范围0-15\n",
    "   3. 目标值通常不用缩放\n",
    "   4. 同比缩放所有属性，2种方法，最小-最大缩放和标准化\n",
    "   5. 最小-最大缩放，又叫归一化，将值重新缩放到0-1之间，将值减去最小值并除以最大值和最小值的差，如果你不希望是0-1可以调整超参数fearture_range\n",
    "   6. 标准化 减去平均值，所以标准化均值总是0，然后除以方差。结果的分布具备单位方差，不同于归一化，标准化不将值绑定特定范围，受影响小\n",
    "   7. 缩放器仅用来拟合训练集，不是完成的数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 转换流水线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 许多数据转换的步骤需要以正确的顺序来执行，Pipeline支持这样的转换\n",
    "# pipline构造函数会通过一系列名称/估算器的配对来定义步骤的序列，必须是转化器，必须有fit_fransform()方法\n",
    "# 调用流出茨的fit方法时，会在所有转换器上按照顺序依次调用fit_transform(),将一个调用的输出，作为参数传递给下一个调用方法，直到传到最终\n",
    "# 估算器，只会调用fit方法\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy = 'median')),\n",
    "    ('attribs_adder', CombinedAttributesAdder()),\n",
    "    ('std_sacler', StandardScaler())\n",
    "]\n",
    ")\n",
    "\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income income_cat  \n",
       "17606       710.0       339.0         2.7042          2  \n",
       "18632       306.0       113.0         6.4214          5  \n",
       "14650       936.0       462.0         2.8621          2  \n",
       "3230       1460.0       353.0         1.8839          2  \n",
       "3555       4459.0      1463.0         3.0347          3  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataFrame→ series→ndarray\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y = None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n",
       " 'latitude',\n",
       " 'housing_median_age',\n",
       " 'total_rooms',\n",
       " 'total_bedrooms',\n",
       " 'population',\n",
       " 'households',\n",
       " 'median_income',\n",
       " 'income_cat']"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_attribs =list(housing_num)\n",
    "num_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline([\n",
    "    ('selector', DataFrameSelector(num_attribs)),  #转为ndarray类型\n",
    "    ('imputer', SimpleImputer(strategy = \"median\")),   # 缺失值的补充\n",
    "    ('attribs_adder', CombinedAttributesAdder()),  # 合成属性\n",
    "    ('std_sacler', StandardScaler()),   # 标准化缩放\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin\n",
    "class MylabelBinarizer(TransformerMixin):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        self.encoder = LabelBinarizer(*args, **kwargs)\n",
    "    def fit(self, x, y = 0):\n",
    "        return self.encoder.fit(x)\n",
    "    def transform(self, x, y = 0):\n",
    "        return self.encoder.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
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       "      <th>ocean_proximity</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
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       "      <td>38.0</td>\n",
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       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
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       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
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       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "17606       710.0       339.0         2.7042       <1H OCEAN          2  \n",
       "18632       306.0       113.0         6.4214       <1H OCEAN          5  \n",
       "14650       936.0       462.0         2.8621      NEAR OCEAN          2  \n",
       "3230       1460.0       353.0         1.8839          INLAND          2  \n",
       "3555       4459.0      1463.0         3.0347       <1H OCEAN          3  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_attribs = ['ocean_proximity']\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "    ('slector', DataFrameSelector(cat_attribs)),\n",
    "    ('LabelBinarizer', MylabelBinarizer()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion  #特征合并\n",
    "\n",
    "full_pipeline = FeatureUnion(transformer_list =[\n",
    "    ('num_pipline', num_pipeline),\n",
    "    ('cat_pipline', cat_pipeline),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_finshed = full_pipeline.fit_transform(housing)\n",
    "housing_finshed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.156043</td>\n",
       "      <td>0.771950</td>\n",
       "      <td>0.743331</td>\n",
       "      <td>-0.493234</td>\n",
       "      <td>-0.445438</td>\n",
       "      <td>-0.636211</td>\n",
       "      <td>-0.420698</td>\n",
       "      <td>-0.614937</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.312055</td>\n",
       "      <td>-0.086499</td>\n",
       "      <td>0.155318</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.176025</td>\n",
       "      <td>0.659695</td>\n",
       "      <td>-1.165317</td>\n",
       "      <td>-0.908967</td>\n",
       "      <td>-1.036928</td>\n",
       "      <td>-0.998331</td>\n",
       "      <td>-1.022227</td>\n",
       "      <td>1.336459</td>\n",
       "      <td>1.890305</td>\n",
       "      <td>0.217683</td>\n",
       "      <td>-0.033534</td>\n",
       "      <td>-0.836289</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.186849</td>\n",
       "      <td>-1.342183</td>\n",
       "      <td>0.186642</td>\n",
       "      <td>-0.313660</td>\n",
       "      <td>-0.153345</td>\n",
       "      <td>-0.433639</td>\n",
       "      <td>-0.093318</td>\n",
       "      <td>-0.532046</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>-0.092405</td>\n",
       "      <td>0.422200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.017068</td>\n",
       "      <td>0.313576</td>\n",
       "      <td>-0.290520</td>\n",
       "      <td>-0.362762</td>\n",
       "      <td>-0.396756</td>\n",
       "      <td>0.036041</td>\n",
       "      <td>-0.383436</td>\n",
       "      <td>-1.045566</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.079661</td>\n",
       "      <td>0.089736</td>\n",
       "      <td>-0.196453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.492474</td>\n",
       "      <td>-0.659299</td>\n",
       "      <td>-0.926736</td>\n",
       "      <td>1.856193</td>\n",
       "      <td>2.412211</td>\n",
       "      <td>2.724154</td>\n",
       "      <td>2.570975</td>\n",
       "      <td>-0.441437</td>\n",
       "      <td>-0.006202</td>\n",
       "      <td>-0.357834</td>\n",
       "      <td>-0.004194</td>\n",
       "      <td>0.269928</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0 -1.156043  0.771950  0.743331 -0.493234 -0.445438 -0.636211 -0.420698   \n",
       "1 -1.176025  0.659695 -1.165317 -0.908967 -1.036928 -0.998331 -1.022227   \n",
       "2  1.186849 -1.342183  0.186642 -0.313660 -0.153345 -0.433639 -0.093318   \n",
       "3 -0.017068  0.313576 -0.290520 -0.362762 -0.396756  0.036041 -0.383436   \n",
       "4  0.492474 -0.659299 -0.926736  1.856193  2.412211  2.724154  2.570975   \n",
       "\n",
       "          7         8         9        10        11   12   13   14   15   16  \n",
       "0 -0.614937 -0.954456 -0.312055 -0.086499  0.155318  1.0  0.0  0.0  0.0  0.0  \n",
       "1  1.336459  1.890305  0.217683 -0.033534 -0.836289  1.0  0.0  0.0  0.0  0.0  \n",
       "2 -0.532046 -0.954456 -0.465315 -0.092405  0.422200  0.0  0.0  0.0  0.0  1.0  \n",
       "3 -1.045566 -0.954456 -0.079661  0.089736 -0.196453  0.0  1.0  0.0  0.0  0.0  \n",
       "4 -0.441437 -0.006202 -0.357834 -0.004194  0.269928  1.0  0.0  0.0  0.0  0.0  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = pd.DataFrame(housing_finshed)\n",
    "housing_prepared.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择和训练模型\n",
    "   1. 获得数据\n",
    "   2. 数据探索\n",
    "   3. 对训练集及测试集拆分\n",
    "   4. 编写转换数据流水线\n",
    "   5. 自动清理和准备机器学习算法的数据\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17606    286600.0\n",
       "18632    340600.0\n",
       "14650    196900.0\n",
       "3230      46300.0\n",
       "3555     254500.0\n",
       "           ...   \n",
       "6563     240200.0\n",
       "12053    113000.0\n",
       "13908     97800.0\n",
       "11159    225900.0\n",
       "15775    500001.0\n",
       "Name: median_house_value, Length: 16512, dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(housing_prepared)\n",
    "df.head()\n",
    "housing_lables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型和评估训练集\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_lables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions [203682.37379543 326371.39370781 204218.64588245  58685.4770482\n",
      " 194213.06443039]\n"
     ]
    }
   ],
   "source": [
    "# 预测数据\n",
    "some_data = housing.iloc[:5]\n",
    "some_lables = housing_lables.iloc[:5]\n",
    "\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "\n",
    "print('Predictions', lin_reg.predict(some_data_prepared))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68376.64295459937"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error    #模型验证 \n",
    "\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "lin_mse = mean_squared_error(housing_lables, housing_predictions)    \n",
    "lin_mse\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 大多数地区房屋的中位数在120000到265000美元之间，预测误差高达68376，这是一个典型的模型对训练集数据拟合不足的案例\n",
    "   2. 原因可能是特征无法提供足够的信息来做出更好的预测，或者模型本身不够强大\n",
    "   3. a.选择强大的模型， b.为算法提供更好的特征 c.减少对模型的限制等方法\n",
    "   \n",
    "   ### 决策树可以找到复杂的非线性关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
       "                      max_features=None, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                      random_state=42, splitter='best')"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor   #决策树模型 训练\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(random_state = 42)\n",
    "tree_reg.fit(housing_prepared, housing_lables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(housing_lables, housing_predictions)\n",
    "tree_mse = np.sqrt(tree_mse)\n",
    "tree_mse   #过拟合，需要再次验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 完美，可能是这个模型对数据严重过度拟合了，如何确认？ 不用轻易启动测试集，拿训练集中的一部分用于训练，另一部分用于模型的验证"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用交叉验证来更好的进行评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([70274.7991723 , 67258.3624668 , 71350.42593227, 68882.91340979,\n",
       "       70987.99296566, 74177.52037059, 70788.57311306, 70850.53018019,\n",
       "       76430.62239321, 70212.6471067 ])"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用train_test_split函数将训练集分为较小的训练集和验证集，然后根据这些较小的训练集来训练模型，并对其进行评估\n",
    "# sklearn的交叉验证，将训练集随机分为10个不同的子集，每个子集称为一个折叠，对模型进行10次训练和评估，每次挑选1个折叠进行评估，另外9个进行训练\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# neg_mean_squared_error是均方差回归损失 该统计参数是预测数据和原始数据对应点误差的平方和的均值\n",
    "\n",
    "#决策树模型的交叉验证\n",
    "scores = cross_val_score(tree_reg, housing_prepared, housing_lables,\n",
    "                       scoring = \"neg_mean_squared_error\", cv= 10)\n",
    "tree_rmse_scores = np.sqrt(-scores)\n",
    "tree_rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [70274.7991723  67258.3624668  71350.42593227 68882.91340979\n",
      " 70987.99296566 74177.52037059 70788.57311306 70850.53018019\n",
      " 76430.62239321 70212.6471067 ]\n",
      "Mean: 71121.4387110585\n",
      "Standard deviation: 2434.3080046605132\n"
     ]
    }
   ],
   "source": [
    "def display_scores(scores):\n",
    "    print('Scores:', scores)\n",
    "    print('Mean:', scores.mean())\n",
    "    print('Standard deviation:', scores.std())\n",
    "    \n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [66877.52325028 66608.120256   70575.91118868 74179.94799352\n",
      " 67683.32205678 71103.16843468 64782.65896552 67711.29940352\n",
      " 71080.40484136 67687.6384546 ]\n",
      "Mean: 68828.99948449328\n",
      "Standard deviation: 2662.761570610345\n"
     ]
    }
   ],
   "source": [
    "# 线性模型的交叉验证\n",
    "lin_scores = cross_val_score(lin_reg, housing_prepared, housing_lables,\n",
    "                           scoring = \"neg_mean_squared_error\", cv= 10)\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=10, n_jobs=None, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 随机森林模型\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators = 10, random_state = 42)\n",
    "forest_reg.fit(housing_prepared, housing_lables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22107.0888400092"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = forest_reg.predict(housing_prepared)\n",
    "forest_mse = mean_squared_error(housing_lables, housing_predictions)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [51481.61843757 48867.18698326 53592.93924497 54917.35753217\n",
      " 50463.39403515 56776.52132037 51940.08691385 50521.84102811\n",
      " 55729.5024898  53136.5656865 ]\n",
      "Mean: 52742.701367175265\n",
      "Standard deviation: 2412.0829538615826\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "forset_scores = cross_val_score(forest_reg, housing_prepared, housing_lables,\n",
    "                           scoring = \"neg_mean_squared_error\", cv= 10)\n",
    "forest_rmse_scores = np.sqrt(-forset_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型调参和网络搜索\n",
    "   1. 手动调整超参数，找到很好的组合很困难。\n",
    "   2. 使用GridSearchCV代替我们搜索，告诉它，进行试验的超参数是什么，和需要尝试的值，它会使用交叉验证评估所有超参数的可能组合。\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
       "                                             criterion='mse', max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             max_samples=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators=100, n_jobs=None,\n",
       "                                             oob_score=False, random_state=42,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 网格搜索\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {'n_estimators':[3, 10, 30], 'max_features':[2, 4, 6, 8]},\n",
    "    {'bootstrap':[False], 'n_estimators':[3, 10], 'max_features':[2, 3, 4]},\n",
    "]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state = 42)\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv =5,\n",
    "                          scoring = 'neg_mean_squared_error', return_train_score = True)\n",
    "grid_search.fit(housing_prepared, housing_lables)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 6, 'n_estimators': 30}"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features=6, max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=30, n_jobs=None, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64246.880700613496 {'max_features': 2, 'n_estimators': 3}\n",
      "55869.85367924813 {'max_features': 2, 'n_estimators': 10}\n",
      "53472.1282558969 {'max_features': 2, 'n_estimators': 30}\n",
      "61376.36445082522 {'max_features': 4, 'n_estimators': 3}\n",
      "53846.329115303764 {'max_features': 4, 'n_estimators': 10}\n",
      "51270.1941502407 {'max_features': 4, 'n_estimators': 30}\n",
      "59860.61532587693 {'max_features': 6, 'n_estimators': 3}\n",
      "53114.42460001889 {'max_features': 6, 'n_estimators': 10}\n",
      "50811.43543872171 {'max_features': 6, 'n_estimators': 30}\n",
      "59220.31563298743 {'max_features': 8, 'n_estimators': 3}\n",
      "52884.78697544277 {'max_features': 8, 'n_estimators': 10}\n",
      "50944.39369116168 {'max_features': 8, 'n_estimators': 30}\n",
      "62805.52917192821 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54462.1410888642 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "61117.32056104296 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "53022.992252269294 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "60234.58052562756 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "52712.989031117104 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "cvres = grid_search.cv_results_\n",
    "for mean_score, params in zip(cvres['mean_test_score'], cvres['params']):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   ccp_alpha=0.0,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   max_samples=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators=100,\n",
       "                                                   n_jobs=None, oob_score=Fals...\n",
       "                   iid='deprecated', n_iter=10, n_jobs=None,\n",
       "                   param_distributions={'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001BFA9E26488>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001BFA9E26A08>},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
       "                   verbose=0)"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "    'n_estimators':randint(low=1, high=200),\n",
    "    'max_features':randint(low=1, high=8),\n",
    "}\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state = 42)\n",
    "rnd_search = RandomizedSearchCV(forest_reg, param_distributions = param_distribs,\n",
    "                                   n_iter=10, cv=5, scoring = 'neg_mean_squared_error', random_state=42)\n",
    "rnd_search.fit(housing_prepared, housing_lables)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析最佳模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.79326113e-02, 6.18280724e-02, 4.33395023e-02, 1.81017027e-02,\n",
       "       1.83291556e-02, 1.93269892e-02, 1.78369580e-02, 2.41360490e-01,\n",
       "       1.61976585e-01, 5.35982558e-02, 1.06273526e-01, 6.14045141e-02,\n",
       "       1.22353255e-02, 1.08821239e-01, 2.76143239e-05, 2.59938294e-03,\n",
       "       5.00807682e-03])"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.24136048955382883, 'median_income'),\n",
       " (0.16197658459849276, 'income_cat'),\n",
       " (0.10882123891274476, 'INLAND'),\n",
       " (0.10627352591969835, 'polplation_per_household'),\n",
       " (0.06793261134305181, 'longitude'),\n",
       " (0.06182807241916786, 'latitude'),\n",
       " (0.061404514078416045, 'bedrooms_per_room'),\n",
       " (0.05359825584988402, 'rooms_per_household'),\n",
       " (0.04333950231438806, 'housing_median_age'),\n",
       " (0.019326989179411204, 'population'),\n",
       " (0.01832915558242795, 'total_bedrooms'),\n",
       " (0.01810170268968371, 'total_rooms'),\n",
       " (0.01783695799011688, 'households'),\n",
       " (0.012235325483341324, '<1H OCEAN'),\n",
       " (0.0050080768169210735, 'NEAR OCEAN'),\n",
       " (0.002599382944523225, 'NEAR BAY'),\n",
       " (2.7614323902184926e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# num_attribs\n",
    "extra_attribs = ['rooms_per_household', 'polplation_per_household', 'bedrooms_per_room']\n",
    "cat_one_hot_attribs = list(encoder.classes_)\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "sorted(zip(feature_importances, attributes), reverse = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 通过测试集评估系统\n",
    "   1. 从测试集中获取预测器和标签\n",
    "   2. 运行full_pipline来转换数据\n",
    "   3. 在测试集上评估最终模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49040.18609727207"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "x_test = strat_test_set.drop('median_house_value', axis = 1)\n",
    "y_test = strat_test_set['median_house_value'].copy()\n",
    "\n",
    "x_test_prepared = full_pipeline.transform(x_test)\n",
    "# x_test_prepared\n",
    "\n",
    "# df = pd.DataFrame(x_test_prepared)\n",
    "# df.head()\n",
    "\n",
    "final_predictions = final_model.predict(x_test_prepared)\n",
    "\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 项目启动\n",
    "   1. 展示解决方案，学习了什么\n",
    "   2. 什么有用\n",
    "   3. 什么没有用\n",
    "   4. 基于什么假设\n",
    "   5. 系统的限制\n",
    "   6. 制作ppt，"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 启动，监控和维护系统\n",
    "   1. 为生产环境做准备，将生产数据接入系统\n",
    "   2. 编写监控代码，定期检查系统的实时性能，性能下降时触发警报，系统崩溃和性能退化\n",
    "   3. 定期使用新数据训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 评估系统性能\n",
    "   1. 对系统的预测结果进行抽样评估，需要人工分析\n",
    "   2. 评估输入系统的数据质量\n",
    "   3. 使用新鲜数据定期训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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